Pollen-based biome reconstructions for Latin America at 0

Clim. Past, 5, 725–767, 2009
www.clim-past.net/5/725/2009/
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
Climate
of the Past
Pollen-based biome reconstructions for Latin America at 0, 6000
and 18 000 radiocarbon years ago
R. Marchant1 , A. Cleef2 , S. P. Harrison3 , H. Hooghiemstra2 , V. Markgraf4 , J. van Boxel2 , T. Ager5 , L. Almeida6 ,
R. Anderson7 , C. Baied8 , H. Behling9 , J. C. Berrio10 , R. Burbridge10 , S. Björck11 , R. Byrne12 , M. Bush13 ,
J. Duivenvoorden2 , J. Flenley14 , P. De Oliveira15 , B. van Geel2 , K. Graf16 , W. D. Gosling17 , S. Harbele18 ,
T. van der Hammen19 , B. Hansen20 , S. Horn21 , P. Kuhry22 , M.-P. Ledru23 , F. Mayle24 , B. Leyden25 ,
S. Lozano-Garcı́a26 , A. M. Melief27 , P. Moreno28 , N. T. Moar29 , A. Prieto30 , G. van Reenen2 , M. Salgado-Labouriau31 ,
F. Schäbitz32 , E. J. Schreve-Brinkman33 , and M. Wille34
1 The
York Institute for Tropical Ecosystem Dynamics (KITE), Environment Department, University of York, York,
Heslington, YO10 5DD, UK
2 Institute for Biodiversity and Ecosystem Dynamics (IBED), Faculty of Science, University of Amsterdam, Postbus 94062,
1090 GB Amsterdam, The Netherlands
3 Bristol Research Initiative for the Dynamic Global Environment (BRIDGE), School of Geographical Sciences, University
Road, University of Bristol, Bristol BS8 1SS, UK
4 INSTAAR, University of Colorado, Boulder, CO 80309, USA
5 USGS, National Centre, MS 970, Reston, VA 22092, USA
6 Laboratorio Biogeografı́a, Facultad de Ciencias, Universidad Nacional Autónoma de México, Aptdo Postal 70-296, 04510
México D.F., Mexico
7 Department of Geography, University of Montana, Missoula, Montana 59812-1018, USA
8 Environmental Studies Program, University of Montana, Missoula Montana 59812, USA
9 Georg-August-Universität, Albrecht-von-Haller-Institut für Pflanzenwissenschaften, Abteilung Palynologie und
Klimadynamik, Untere Karspüle 2, 37073 Göttingen, Germany
10 Department of Geography, University Road, University of Leicester, LE1 7RH, UK
11 Geological Institute, University of Copenhagen, Øster Volgade 10, 1350 Copenhagen, Denmark
12 Department of Geography, University of California, Berkeley, California 94720-4740, USA
13 Dept. of Biological Sciences, Florida Institute of Technology, 150 W. University Boulevard, Melbourne, FL 32905, USA
14 Department of Geography, Massey University, Palmerston, New Zealand
15 Instituto de Geociencias-DPE, Universidade de São Paulo, Caixa Postal 11348, São Paulo, SP 05422-970, Brazil
16 Geographisches Institut der Universität, Winterthürerstraße 190, 8057 Zürich, Switzerland
17 Dept. of Earth and Environmental Sciences, CEPSA, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK
18 Department of Archaeology and Natural History, College of Asia and the Pacific, Australian National University, Canberra,
ACT 0200, Australia
19 Fundación Tropenbos Colombia, Carrera 21 # 39-35, Santafe de Bogotá, Colombia
20 Limnological Research Centre, University of Minnesota, 220 Pillsbury Hall, 310 Pillsbury Drive, Minneapolis, MN
55455-0219, USA
21 Arctic Centre, University of Lapland, PL 122, 96101 Rovaniemi, Finland
22 Department of Geography, University of Tennessee, 408 G&G Building, Knoxville, TN 37996-1420, USA
23 Equipe Paléoenvironements, Institut des Sciences de l’Evolution Institut de Recherche pour le Developement, Montpellier,
France
24 Geography Building, Drummond Street, Edinburgh, EH8 9XP, UK
25 Department of Geology, University of South Florida, 4202 East Fowler Avenue, SCA 203, Tampa, FL 33620-5200, USA
26 Universidad Nacional Autónoma de México, Instituto de Geologı́a, Aptdo Postal 70-296, 04510 México D.F., Mexico
27 2E Oosterparkstraat 163A, 1092 BE Amsterdam, The Netherlands
28 Facultad de Ciencias, Universidad de Chile, Casilla 653, Santiago, Chile
29 Botany Division, D.S.I.R., Private Bag, Christchurch, New Zealand
30 Laboratorio de Palinologia, National Universidad Mar del Plata, Dept. de Biologia, Funes 3250, 7600 Mar del Plata,
Argentina
31 Inst. de Geociencias, Fundação Universidade do Brazilia, Campus Universitario, Asa Norte, 0910-900, DF Brazilia, Brazil
Published by Copernicus Publications on behalf of the European Geosciences Union.
726
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
32 Geographisches
Institut, Universität Bamberg, Am Kranen 1, 96045 Bamberg, Germany
8, 7495 SZ Delden, The Netherlands
34 Geographisches Institut, Universität Essen, Essen, Germany
33 Welbergsweg
Received: 13 October 2008 – Published in Clim. Past Discuss.: 10 February 2009
Revised: 7 August 2009 – Accepted: 29 September 2009 – Published: 1 December 2009
Abstract. The biomisation method is used to reconstruct
Latin American vegetation at 6000±500 and 18 000±1000
radiocarbon years before present (14 C yr BP) from pollen
data. Tests using modern pollen data from 381 samples derived from 287 locations broadly reproduce potential natural
vegetation. The strong temperature gradient associated with
the Andes is recorded by a transition from high altitude cool
grass/shrubland and cool mixed forest to mid-altitude cool
temperate rain forest, to tropical dry, seasonal and rain forest at low altitudes. Reconstructed biomes from a number
of sites do not match the potential vegetation due to local
factors such as human impact, methodological artefacts and
mechanisms of pollen representivity of the parent vegetation.
At 6000±500 14 C yr BP 255 samples are analysed
from 127 sites. Differences between the modern and
the 6000±500 14 C yr BP reconstruction are comparatively
small; change relative to the modern reconstruction are
mainly to biomes characteristic of drier climate in the north
of the region with a slight more mesic shift in the south. Cool
temperate rain forest remains dominant in western South
America. In northwestern South America a number of sites
record transitions from tropical seasonal forest to tropical dry
forest and tropical rain forest to tropical seasonal forest. Sites
in Central America show a change in biome assignment, but
to more mesic vegetation, indicative of greater plant available
moisture, e.g. on the Yucatán peninsula sites record warm evergreen forest, replacing tropical dry forest and warm mixed
forest presently recorded.
At 18 000±1000 14 C yr BP 61 samples from 34 sites
record vegetation reflecting a generally cool and dry environment. Cool grass/shrubland is prevalent in southeast Brazil
whereas Amazonian sites record tropical dry forest, warm
temperate rain forest and tropical seasonal forest. Southernmost South America is dominated by cool grass/shrubland,
a single site retains cool temperate rain forest indicating that
forest was present at some locations at the LGM. Some sites
in Central Mexico and lowland Colombia remain unchanged
in the biome assignments of warm mixed forest and tropical
dry forest respectively, although the affinities that these sites
have to different biomes do change between 18 000±1000
Correspondence to: R. Marchant
([email protected])
Clim. Past, 5, 725–767, 2009
14 C
yr BP and present. The “unresponsive” nature of these
sites results from their location and the impact of local
edaphic influence.
1
Introduction
Biomisation is an objective method to reconstruct broad vegetation types based on the assignment of pollen taxa to
one or more plant functional types (PFTs) (Prentice et al.,
1996a). The method is based on the assumption that a pollen
spectrum will have different degrees of affinity to different
biomes that can be quantified by a simple algorithm. Biome
reconstructions from pollen data at 6000±500 14 C yr BP and
the last glacial maximum (LGM) at 18 000±1000 14 C yr BP
have been produced for most regions of the world under the
auspices of the BIOME 6000 project (Prentice et al., 1998,
2000). The validity of the method in reconstructing biomes
at different time intervals has been demonstrated for Africa
(Jolly et al., 1998a; Elenga et al., 2000), Australia (Pickett
et al., 2004) Beringia (Bigelow et al., 2003; Edwards et al.,
2000), China (Yu et al., 1998, 2001), Eastern North America (Williams et al., 2000), Eurasia (Tarasov et al., 1998a),
Europe (Prentice et al., 1996a, b; Tarasov et al., 1998a, b;
Elenga et al., 2000), Japan (Takahara et al., 2001) and Western North America (Thompson and Anderson, 2000). Results from Latin America, presented here, represent the last
geographically large area to undergo this process. Within
Latin America the biomisation method has been previously
applied to Colombian and Mexican pollen data at a range
of spatial and temporal scales; from the middle Holocene
(Marchant et al., 2001a; Ortega-Rosas et al., 2008a, b), the
LGM (Marchant et al., 2002a), to investigate modern-pollen
vegetation relationships (Marchant et al., 2001b), impact of
human societies on vegetation (Marchant et al., 2004a) and
as a basis for comparisons with output from a vegetation
model run under different climatic and environmental scenarios (Marchant et al., 2004b, 2006). In addition to these
spatial investigations, the method has been applied downcore down to a 450 000 year pollen record from the high
plain of Bogotá (Marchant et al., 2002b). As Colombia is
biogeographically complex, encompasses high altitude, temperate and tropical floras reflecting a range of environmental
space including transitions from hyper-humid to semi-arid
climates, these analyses provided a suitable test-bed for the
wider geographical focus presented here.
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Figure 1. Map of Latin America depicting the present-day summer and winter
position of the ITCZ and the macro-scale wind (and hence moisture) patterns
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
over Latin America.
In addition to reconstructing vegetation patterns, and investigating factors that can explain observed changes, data on
past biomes contributes to testing of climate and vegetation
models (Prentice et al., 1992; Haxeltine and Prentice, 1996;
Peng et al., 1998; Marchant et al., 2006; Braconnot et al.,
2007). Vegetation models can be used to portray output from
Global Circulation Models (GCMs) as maps of potential vegetation (Claussen and Esh, 1994; Foley et al., 1996; Prentice
et al., 1996b; Williams et al., 1998) that can be used in the development of models that couple biosphere, atmosphere and
oceanic components (Braconnot et al., 2007; Claussen, 1994;
Harrison et al., 2003; Texier et al., 1997) and testing of biogeochemical dynamics (Peng et al., 1998). There has been
growing interest has focused how atmosphere-biosphere interactions have operated under the changing environmental
conditions since the LGM, particularly in trying to understand the response of ecosystems to different types of environmental forcing (Jolly and Haxeltine, 1997). Transformed
pollen data can further be used in conjunction with other data
types, such as on lake status (Jolly et al., 1998b) and archaeological evidence (Piperno et al., 1990, 1991a, b), to better
understand the causal factors driving vegetation change over
the recent geological past.
1.1
Latin American region
Latin America comprises the area from 35◦ N to 65◦ S,
and from 35◦ W to 120◦ W extending from México to islands off southernmost South America from Eastern Brazil
to the Galapagos Islands. Latin America is characterised
by strong environmental gradients associated with 100◦ of
latitude, approximately 7000 m of altitude and the transition from oceanic- to continentally-dominated climate systems (Fig. 1). Despite this great extent there has been permanent contact between the tropical and temperate domains
throughout geological time. Physiographically, Latin America is characterised by stable cratons associated with the interior and areas of active mountain building, particularly associated with the Andes. This environmental variability is reflected by an incredibly diverse biogeography, ranging from
the highly diverse rain forest of the Chocó Pacific (Colombia) to the cold deserts of the high Andes, from the hot semidesert areas of México to the cold moorlands of Tierra del
Fuego (Fig. 2). Descending an altitudinal gradient there is a
transition from páramo (cool grass/shrubland ) to high Andean forests (cool mixed and cool temperate rain forests) and
lower Andean forest (warm evergreen forest) (Fig. 3). Complicating this potential vegetation distribution is the factor of
human impact with the majority of the vegetation in Latin
America being impacted on by the vegetation (Ellis and Ramankutty, 2008). The timing of early human settlement in
Latin America is a contentious subject, although it seems
from the early Holocene there was considerable cultural diversity and adaptation to a series of different environments
(Gnécco, 1999). Human-induced impact has had a direct inwww.clim-past.net/5/725/2009/
110
90
70
727
50
ITCZ position July
ITCZ position January
Prevailing wind direction
20
0
20
40
Fig. 1. Map of Latin America depicting the present-day summer and
winter position of the ITCZ and the macro-scale wind (and hence
moisture) patterns over Latin America.
fluence on vegetation composition and distribution through
land-use practices and the introduction of alien taxa and cultivars to the Latin American flora. For example, in excess
of 100 plants were under cultivation prior to the European
conquests in the 15th century (Piperno et al., 2000).
1.2
Latin America climate
Cerverny (1998), Eidt (1968) and Metcalfe et al. (2000) have
reviewed Latin American climate. Given the broad geographical scope, Latin America is characterised by a variety of climates that relates to its global position, shape of
the landmass, location and height of the Andes, offshore currents, general hemisphere air flow and proximity of large water bodies (Fig. 1). Four dominant circulation regimes influence Latin America: the Inter-Tropical Convergence Zone
(ITCZ), the prevailing westerlies, the semi-permanent high
pressure cells located over the South Pacific and South Atlantic Oceans and the trade winds. Perhaps most dominant is the annual oscillation of the meteorological equator
(ITCZ), this migrating some 10–15◦ latitude about the equator (Fig. 1). The ITCZ reaches its northernmost location in
June, this bringing high rainfall for northern South America and the Caribbean, with January and February recording the dry season (Cerveny, 1998). However, due to the
influence of the westerlies from the Pacific, and the sharply
rising topography of the Andes, the ITCZ has a sinusoidal
Clim. Past, 5, 725–767, 2009
728
Figure 2. Map of the modern potential vegetation as derived from Schmithüsen, J. (1976)
and Hück (1960). For example, the various divisions of seasonally dry forest such as
cerradõ, caatinga, campo rupstre, savanna, are combined to the biome of tropical dry
forest. Acronyms are explainedR.inMarchant
Table 1. et al.: Pollen-based biome reconstructions for Latin America
Fig. 2. Map of the modern potential vegetation as derived from Schmithüsen (1976) and Hück (1960). For example, the various divisions
of seasonally dry forest such as cerrado, caatinga, campo rupstre, savanna, are combined to the biome of tropical dry forest. Acronyms are
explained in Table 1.
profile over northwestern South America (Fig. 1). In southern South America the prevailing westerlies south of 40◦ S
are particularly important in controlling the moisture regime.
The topographic barrier of the Andes contributes to the creation of two large semi-present anticyclones, one over the
South Pacific and one over the South Atlantic, the southeast
trade winds associated with this latter system brings abundant moisture to the Amazon Basin (Cerveny, 1998). Due to
the large size of South America, and the highland ranges that
fringe much of the continent there is often a rapid transition
from relatively moist coastal areas to a dry interior reflecting the transition from oceanic- to continental-dominated climate systems. For example, due to the proximal location of
the Pacific-based moisture source and steeply rising ground,
Clim. Past, 5, 725–767, 2009
precipitation is highest (>15 000 mm yr−1 ) in the Chocó Pacific region. Exceptions to this scenario are areas located
between the anticyclones, e.g. the Peruvian coast, where relatively arid conditions prevail.
One of the main environmental gradients in Latin America is associated with the Andes. The Andes are characterised by a diurnal climate (Kuhry, 1988); at a given location differences in monthly temperature are small (<3◦ C)
although daily fluctuations may be large (20◦ C), especially
during the dry seasons. Climatic changes with altitude can
be summarised as a lapse rate (Barry and Chorley, 1990).
Applying a lapse rate of 6.6◦ C 1000 m−1 (Van der Hammen
and González, 1965; Wille et al., 2001), this altitudinal rise
equates to a temperature change of more than 30◦ C. Also
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(a, b) to cool mixed forest (c, d, e, f), tropical dry forest that can be dominated by Bauhina (g, h) with
dominance of open steppe grasslands (i) tropical rain forest (j, k), dominated by mangrove in areas
fringing saline water (l), tropical seasonal forest (m), cool temperate forest (n, o). The bottom plate
(p) shows the importance of edpahic factors on controlling vegetation, in this case local hydrology
R. Marchant et where
al.: Pollen-based
reconstructions
for Latin
America
rill channels biome
allow trees
to grow in areas
that would
be dominated by grassland.
729
A
B
C
D
E
F
G
H
I
J
K
M
N
O
L
P
Fig. 3. Examples of Latin America biomes from cool grass shrubland with Scencio and Espeletia (a, b) to cool mixed forest (c, d, e, f),
tropical dry forest that can be dominated by Bauhina (g, h) with dominance of open steppe grasslands (i) tropical rain forest (j, k), dominated
by mangrove in areas fringing saline water (l), tropical seasonal forest (m), cool temperate forest (n, o). The bottom plate (p) shows the
importance of edpahic factors on controlling vegetation, in this case local hydrology where rill channels allow trees to grow in areas that
would be dominated by grassland.
associated with the Andes are steep gradients of moisture
availability. Rainfall is high on the eastern slopes of the Andes; the concave nature acting as a receptacle for moisture
transferred by the southeast trade winds from the Atlantic
Ocean, in part receiving moisture generated by the Amazonian forest (Fjeldså, 1993). Low rainfall is recorded within
rain shadow areas, such as on the lower slopes of the Magdalena Valley and the inter-Andean plains (Kuhry, 1988).
These climate gradients result in rapid transitions from mesic
to xeric vegetation types, e.g., cool high-altitude grasslands
change to “temperate” forests at mid-altitudes and diverse
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tropical rain forests within a few kilometres (Fig. 3). In the
southern part of Latin America rainfall is largely controlled
by the persistence and strength of the westerly winds (Gilli
et al., 2005). There has been increased interest in largescale temperature-driven surface pressure oscillations in the
Pacific Ocean termed the Southern Oscillation, and its assimilated oceanic aspects, El Niño and its antithesis La Niña
(Cerveny, 1998; Godı́nez-Domı́nguez et al., 2000; Metcalfe
et al., 2000), however, given the temporal foci of the vegetation reconstructions here this is not so relevant.
Clim. Past, 5, 725–767, 2009
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R. Marchant et al.: Pollen-based biome reconstructions for Latin America
Table 1. Range of plant functional types identified within the Latin American region giving bioclimatic range and physiological adaptation.
Code
Plant functional type
Bioclimatic range and plant physiological adaption
g
Graminoid
Ecologically broad category that occurs in a number of biomes, a highly adaptive PFT with a ubiquitous distribution and little diagnostic value.
man
Mangrove
Constituent of lowland tropical vegetation, control on distribution is mainly
hydrological
tx
Tree fern
Can be locally dominant. Occupying a broad bioclimatic range, particularly
common in temperate moist areas.
Te1
Tropical broad-leaved evergreen tree
MTCO>15.5◦ C, α>0.7, short dry season (1month), GDD>5000.
Te2
Tropical xeric broad-leaved evergreen tree
MTCO>15.5◦ C, α>0.6–08, longer dry season (2–4 months), GDD>5000,
withstands longer dry season by shedding leaves.
Tr1
Tropical rain green tree
MTCO>15.5◦ C, α>0.9, GDD>5000, present in wettest tropical rain forest.
Tr2
Dry tropical rain green tree
MTCO>15.5◦ C, α0.8–0.9, GDD>5000, present in range of tropical seasonal
forest types.
ctc
Cold temperate conifer
MTCO 5◦ C–15◦ C, α>0.7, GDD>4500, common in the Brazilian highlands.
ctc1
Cool temperate conifer
MTCO −5◦ C–10◦ C, α 0.95–0.75, GDD>900, common along the western
coast of southern South America.
ctc2
Maritime evergreen conifer
MTCO −10◦ C–5◦ C, α>0.65, GDD>1000, common along the western coast
of southern South America.
ec
Eurythermic conifer
MTCO>5◦ C, α 0.4–0.6, GDD>5000, common within dry forest of South
America and Mexico.
txts
Tropical xerophytic tree/shrub
MTCO>20◦ C, α 0.2–0.35, GDD>5000, woody shrubs common in dry forest.
ds
Desert shrub
MTCO>20◦ C, α 0.2–0.35, GDD>5000, woody shrub and cacti in Mexico
and coastal Peru
df
Xerophytic forb
MTCO>20◦ C, α 0.2–0.35, GDD>5000, woody shrub and cacti in Mexico
and coastal Peru
tf
Tropical forb
MTCO>15.5◦ C, α>0.6, GDD>5000, frost intolerant
tef
Temperate forb
MTCO>5◦ C–15◦ C, α>0.6, GDD>1000, frost tolerant
sf
Eurythermic forb
MTCO 5◦ C–10◦ C, α 0.65–0.7, GDD 2500–4000, requires a seasonal moist
environment
af
Arctic forb
MTCO −5◦ C–0◦ C, α 0.05–0.1, GDD<500, frost tolerant
cp
Cushion forb
MTCO <−5◦ C, α<0.2, GDD<500, specific growth form, frost tolerant.
wte
Warm temperate evergreen broad-leaved tree
MTCO 5◦ C–15◦ C, α>0.65, GDD>3000, frost tolerant mesophyllous trees
ts
Temperate summer green tree
MTCO 0◦ C–5◦ C, α>0.65, GDD>2000, frost tolerant micro and mesophyllous trees
ts1
Temperate evergreen broad-leaved tree
MTCO −5◦ C–5◦ C, α>0.5, GDD>1000, sclerophyllous, usually evergreen
wte1
Temperate cool deciduous broad-leaved tree
MTCO 0◦ C–15◦ C, α>0.6, GDD>3000, winter deciduous, requires warm
growing season.
wte4
Temperate cold-deciduous broad-leaved tree
MTCO −5◦ C–5◦ C, α 0.55, GDD is >2500, winter deciduous, requires warm
growing season but this can be short.
aa
Arctic shrub
MTCO −5◦ C–0◦ C, α 0.2–0.4, GDD 500–1000, frost tolerant
Clim. Past, 5, 725–767, 2009
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R. Marchant
Pollen-based
biome
reconstructions
Figure 4. Crosset
anal.:
altitudinal
cross section across
the northern
Andes in Colombiafor Latin America
731
showing the standard vegetation units and their relationship to Biomes (a) and plant
functional types (b). Acronyms are explained in Tables 1 and 2
5000
4000
CGSS
CGSH
(a)
Altitude (m)
COMI
3000
CEFO
WAMF
2000
WEFO
1000
TRFO
TSFO
TDFO
STEP
0
TROPICAL RAIN FOREST
UPPER ANDEAN FOREST
SAVANNA
SUB-PARAMO
LOWER ANDEAN FOREST
PARAMO / SUPER PARAMO
5000
cp
(b)
wte4
af
4000
Altitude (m)
aa
ts
ctc2
3000
wte1
g
ctc1
ec
2000
wte
tf
tx
tf
1000
Tr1
Te1
sf
Te2
Tr2
txts
man
0
Fig. 4. Cross an altitudinal cross section across the northern Andes
in Colombia showing the standard vegetation units and their relationship to Biomes (a) and plant functional types (b). Acronyms
are explained in Tables 1 and 2.
1.3
Latin America vegetation
For the purpose of this investigation the potential vegetation
composition and distribution Latin America is classified at a
coarse resolution with twelve biomes being identified (Fig. 2)
that summarise the 57 categories mapped by Hück (1960)
and 45 by Schmithüsen (1976). This process of reclassifying
the vegetation was carried out in consultation with a number of the co-authors, particularly those with good ecological knowledge and a range of geographical expertise. Reducing the number of vegetation categories results in a significant smoothing of the ecosystem transitions, particularly
when these are very sharp or specific biomes are quite isolated. A good example are the relatively small deserts of
north-western Mexico that get mapped as tropical dry forest
rather than desert. The vegetation composition and distribution generally reflects the main climatic and topographic
gradients described above. However, a series of caveats to
this must be stressed. Firstly, the actual and potential vegetation can be quite different, the former reflecting a long
history of human interaction that has been particularly pronounced since the colonial period but has been influencing
the vegetation for at least the last 5000 years (Marchant et
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al., 2004). In numerous areas this interaction has completely
transformed the potential vegetation to an agricultural landscape. Another factor complicating the relationship between
climate and vegetation is the locally strong edaphic influence
by substrate, topography or geographic character (Fig. 3).
The strength of this influence is demonstrated by areas of
tropical dry forest that form on free-draining sandstones, e.g.
the Llanos Orientales (Colombia); these are located in areas where the climate regime would support tropical seasonal
forest, or even tropical rain forest.
Broad types of vegetation with similar composition and
distribution (biomes) result from a combination of plant
functional types (PFTs). PFTs and biomes, which lie at the
heart of the biomisation technique, allow the high floristic
diversity within the Latin America pollen flora to link with
the relatively coarse vegetation classification (Fig. 4). PFTs
group together species that have common character (Prentice et al., 1992). This grouping is based on common life
form and phenology, combined with the geographic distribution that is in part determined by climate (Woodward, 1987).
An indication of the bioclimatic range of each PFT and plant
physiological adaptation, to the given environmental condition, is presented in Table 1. The range of biomes identified
within the Latin America, floristic description, main location
and equivalent floristic units is portrayed in Table 2. The
cool grass/shrubland biome incorporates a relatively wide
range of vegetation dominated by grasses, heath, cool temperate sclerophyll shrubs and cushion plants (Fig. 3). This
biome is present in southern South America and at high altitudes along the Andes. In addition to the cool grassland a
warm grassland (steppe) is identified. Steppe is found predominately under the warm, dry climates of southeast and
northeast Brazil, northwestern Argentina and coastal northern South America. Warm temperate rain forest represents
a mix of warm conifers such as Araucaria, Andean and Atlantic rain forest elements, whereas cool temperate rain forest
contains cool conifers, such as Fitzroya, Andean and Valdivian rain forest elements. Dry forests are extensive in Latin
America, specifically associated with areas located between
the two semi-permanent anticyclones and influenced by the
high seasonality of rainfall imposed by the annual migration
of the ITCZ. For our classification we characterise the diverse
dry vegetation formations (Fig. 4) as the tropical dry forest
biome. Xerophytic trees and shrubs is widespread in the interior of South America, along the southwestern Pacific coast
and northeast Brazil where it grades into steppe, additionally, there are patches in Colombia, on the Yucatán peninsula
and in México (Fig. 2). Tropical dry forest is predominantly
recorded in two main swaths either side of the Amazon basin,
with an extension through Central America. The tropical seasonal forest biome is predominantly recorded to the north of
Amazonia where it is interspersed with patches of dry forest; this reflecting a strong edaphic influence. A large area
of tropical seasonal forest is recorded away from the hyperhumid area of Brazil along the Atlantic coast. The tropical
Clim. Past, 5, 725–767, 2009
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R. Marchant et al.: Pollen-based biome reconstructions for Latin America
Table 2. Biomes identified within the Latin American region as portrayed in the vegetation map (Fig. 2) indicting a floristic description, the
main location and equivalent floristic units found in a macro scale analysis of the Latin American vegetation.
Code
Biome
Definition
Main locations
Equivalent
Floristic characteristics
TRFO
Tropical
rainforest
Closed canopy lowland evergreen forests.
Canopy
broken
by
emergent trees (>40 m).
MTCO>18◦ C, precipitation >1500 mm yr−1 ,
frost-intolerant.
Characterise much of
the Amazon catchment.
Can form a relatively
thin band along tropical coastal areas, e.g.
Atlantic rain forest of
Brazil, Chocó pluvial
forest of Colombia,
maintained by high
moisture derived from
close
proximity
of
oceanic influence.
Amazonian forest, Tropical moist forest, Atlantic
rain forest, Terra firme
forest, Várzea, Gallery
forest, Chocó pluvial forest.
Generally characterised
by plants with mesophyll leaf, although some
sclerophyllous plants are
present, often tree ferns
and palms.
TSFO
Tropical
seasonal
forest
Relatively tall (20–30 m)
closed canopy forest
with occasionally tall
(>40 m) emergent trees.
Canopy opens in a
mosaic as deciduous
elements loose leaves.
Seasonally dry from 1–4
months.
Dominant to the north of
Amazonian tropical rainforest, in central America and formerly extensive in the interior of
Brazil prior to extensive
clearance.
Marsh forests, savanna
gallery forest, Seasonal
swamp
forest
with
palms.
A mix of mesophyllous
and sclerophyllous taxa.
The structure of the forest is dependent on moisture demand and length
of dry season – this determines the amount of
deciduous taxa. Palms
can be locally common.
TDFO
Tropical dry
forest
Relatively low (5–10 m),
occasionally tall (20 m)
trees.
Mixed forest,
forming where the dry
season leads to drought
and plant water stress.
Extensive in central
Brazil and central South
America adjacent tropical rainforest.
More
fragmented in northwestern South America
where free draining
substrate leads to waterstress. Extends to mid
altitudes,
particularly
within rain shadow
areas.
Extensive in
western Central America
and Mexico. Present on
the Galápagos Islands.
Andean xerophytic bush,
Cerrado, Campo rupestres, Campo cerrado
(“campo” is more associated with grasslands).
Cactus forest, Matorral,
Deciduous xerophytic
forest, Andean xerophytic bush, Espinar,
Restinga dune forests,
Thorn forest, Chaco.
Xeromorphic
characteristic, particularly fire
tolerant. For example,
microphyllous
leaves,
thorns, deciduous leaves,
thick bark, stomata often
present along lines.
Drought adapted taxa
are common, e.g. tree
cacti (Opuntia and Jasminocerus) with dense
undergrowth of shrubs
and herbs.
WTRF
Warm temperate rainforest
Evergreen closed forest,
of relatively low stature
(<20 m) with tall emergent trees (>25 m). Not
tolerant of freezing. A
transitional forest type
between lowland and
higher altitude forms
(1000–2500 m).
Extending along the
Andes at mid to low elevations (500–2000 m).
Present at slightly lower
elevations in eastern
Brazilian
highlands
(<1000 m).
Lower montane forest,
Moist lower montane
forest,
Submontane
forest and Araucariadominated forest also
with Podocarpus.
A mix of mesophyllous
and sclerophyllous taxa
constrained by altitude
and length of dry season.
Palms and tree ferns can
be locally common.
WEFO
Warmtemperate
evergreen
broadleaf
forest
Evergreen semi-closed
forest with tall emergent
trees (>30 m).
Not
tolerant of freezing.
Present within a relatively restricted range
and along the Andes,
particularly present from
1000–2000 m.
Andean forest, Transitional Andean forest,
Upper Andean forest.
A mix of mesophyllous
and sclerophyllous taxa.
Tree ferns can be locally
common.
Clim. Past, 5, 725–767, 2009
www.clim-past.net/5/725/2009/
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
733
Table 2. Continued.
Code
Biome
Definition
Main locations
Equivalent
Floristic characteristics
CTRF
Cool temperate
rainforest
Medium height (<15 m)
closed canopy forest
with a dense under-story.
Can tolerate freezing.
Predominant along western coast of southern
South America extending to Patagonian steppe.
Also present along the
Andes at mid to high altitudes.
Patagonian rain forest,
Temperate rain forest,
Valdivian rain forest,
Magallenic rain forest.
A mix of mesophyllous
and sclerophyllous taxa.
The structure can be
quite variable depending
on location – from dense
forest to scrubby heath.
WAMF
Warm
temperate
mixed forest
Medium height (<15 m)
open canopy with open
under-story. Drought tolerant, semi fire-tolerant.
Mid to high altitudes of
north Central America,
in particular Mexico.
Pinus and Quercusdominated forest.
Mixed evergreen forest
dominated by sclerophyllous taxa that
require warm for budburst.
COMI
Cool mixed
forest
Short stature woodlands
(<5 m) open canopy,
open under-story forest.
Frost tolerant.
High
Andean
shrub/dwarf tree forests,
present close to the
forest line
Upper montane forests,
High Andean forest,
Cloud forest.
Predominantly evergreen
taxa with physiological
adaptation to night frost,
e.g. retaining old leaves
for insulation.
CGSS
Cool grasslands
Common above the
forest line of the Andes,
dominated by tussock
grasses and cushion
plants.
Present only at the highest altitudes of the Andes
Puna, Heath, Cushion
heath.
Poaceae-dominated
cool grasslands with
occasional
cushion
plants
STEP
Steppe
Dominated by grasses,
occasional shrubs and
steppe herbs. Profuse
flowering during the wet
season
Extensive in eastern
Argentina, present in
lowland Central America
and northeast Brazil.
Steppe
grasslands,
Campo limpo, Pampa.
Grasses and chenopods
forming low altitude
warm grasslands.
DESE
Desert
Open semi-arid to arid
vegetation.
Coastal Peru and Chile
and western Mexico.
Coastal desert.
Occurrence of CAMplants, cacti and succulents.
CGSH
Cool grass
shrublands
Tropic-alpine environments, common above
the forest line of the
Andes. A mixture of
tussock grasses and
cold-adapted shrubs.
Present from extreme
southern South America,
on Tierra del Fuego
above the forest line of
Andes (2800–4000 m).
Páramo,
Subpáramo,
Magallenic moorland,
Paramillo, Vegas.
Poaceae-dominated cool
grasslands with numerous tussock forming
grass. Also present are
shrubs, e.g. Empetrum,
Espeletia and Puya.
rain forest biome is present in three main areas: Amazonia, linear strips along the Atlantic coast and northeast South
America extending into Central America. Forest associated
with highland areas is divided into three biomes: warm evergreen forest, cool temperate rain forest and cool mixed forest (Fig. 4). Warm evergreen forest is most extensive along
the lowland Andes, adjacent to the tropical rain forest. Cool
mixed forest has a more restricted distribution, occupying a
highland position until temperature becomes limiting for a
number of taxa. Warm mixed forest is characterised by a
mix of Pinus and Quercus species and is mainly restricted
www.clim-past.net/5/725/2009/
to Central America. The desert biome is restricted to coastal
Peru, due to the Pacific Ocean anticyclone, this area receives
very little moisture.
2
Methods
2.1 Data sources
Over the past five decades researchers have collected numerous pollen-based records from lakes and bogs (Table 3)
Clim. Past, 5, 725–767, 2009
734
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
Table 3. Characteristics of the sites from Latin America: specifically detailing location, potential vegetation around the sites, sample type,
age range of sediments, number of C14 dates (RC), data type, principle analysts and associated references. Dating control (DC) codes are
based on the COHMAP dating control scheme (Webb, 1985; Yu and Harrison, 1995). For sites with a continuous sedimentation (indicated
by C after the numerical code), the dating control is based on bracketing dates as follows. 1: both dates within 2000 years of the selected
interval, 2: one date within 2000 years the other within 4000 years, 3: both dates within 4000 years, 4: one date within 4000 one date within
6000 years, 5: both dates within 6000 years, 6: one date within 6000 the other within 8000 years, 7 bracketing dates more than 8000 years
from the selected interval. For sites with discontinuous sedimentation (indicated by D after the numerical code), the dating control is based
on single dates 1: indicated a date within 250 years of the selected interval, 2: a date within 500 years, 3: a date within 750 years, 4: a date
within 1000 years, 5: a date within 1500 years, 6: a date within 2000 years, 7: a date of more than 2000 years from the selected interval.
Site
Country
Long.
Latitude
Alt.
Age range
Present biome
Sample
type
RC
DC at 6000
yr BP
DC at 18 000
yr BP
Data type
Lake Åsa3
Harberton
Antarctica
Argentina
−61.13
−67.16
−62.62
−54.88
35
20
1280–4980
0–13 360
CGSH
STEP
Soil
Mire
14
16
–
1C
–
–
Puerto del Hambre
La Misión
Punta Arenas
Meseta Latorre 1
Meseta Latorre 2
Torres del Paine
Moreno Glacier Bog
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Dichan
Puerto Eden
Chile
Argentina
Chile
Argentina
Argentina
Chile
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Chile
Chile
−70.92
−67.83
−70.97
−72.05
−72.03
−72.66
−73.00
−72.90
−72.90
−72.70
−72.98
−72.95
−72.90
−72.85
−72.75
−72.80
−72.00
−71.70
−71.50
−71.10
−69.90
−69.30
−71.30
−70.90
−70.20
−69.60
−70.50
−72.55
−71.95
−69.00
−72.90
−68.60
−68.90
−72.00
−68.10
−68.30
−73.88
−74.41
−53.59
−53.5
−53.15
−51.52
−51.44
−50.98
−50.46
−50.25
−50.20
−50.20
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.10
−50.10
−50.10
−50.05
−50.05
−50.05
−50.05
−50.00
−50.00
−49.66
−49.13
5
5
75
980
1000
100
200
50
20
80
20
50
50
60
60
70
150
180
180
180
180
180
190
190
190
190
200
100
180
190
50
50
100
150
0
20
50
50
0–16 000
0–9990
0–16 500
240–7120
0–7000
0–11 000
0–9550
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
Modern
0–8000
0–14 750
CTRF
STEP
CTRF
CGSH
CGSH
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
Mire
Mire
Mire
Mire
Mire
Lake
Mire
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Mire
Mire
5
4
5
3
1
8
2
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
5
7
4C
4C
4C
4C
6D
2C
4C
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
2C
4C
La Esperanza
LagoBsAs
Laguna Stibnite
Chile
Argentina
Chile
−72.83
−71.45
−74.43
−46.63
−46.44
−46.43
330
230
50
0–1500
Modern
0–14 000
CTRF
CTRF
CTRF
Mire
Soil
Lake
–
–
6
Laguna Stibnite
Laguna Six Minutes
Pico Salam
Laguna Lincoln
Laguna Lofel
Estero Huitanque
AustroEsqu
Mayol
Puchilco
AlercesNor
San Pedro
Chepu
Mallin Book
Comallo
Puerto Octay PM13
Espuma
Primavera
Encantado
Ruta 3.4
Ruta 3.3
Caunahue
Ruta 250.19
Chile
Chile
Argentina
Chile
Chile
Chile
Argentina
Chile
Chile
Argentina
Chile
Chile
Argentina
Argentina
Chile
Argentina
Argentina
Argentina
Argentina
Argentina
Chile
Argentina
−74.38
−74.33
−67.43
−74.07
−74.43
−73.82
−71.47
−73.75
−73.62
−71.60
−73.95
−73.66
−71.58
−70.21
−72.90
−63.25
−71.18
−71.13
−62.79
−62.59
−72.00
−65.58
−46.43
−46.43
−45.42
−45.34
−44.85
−43.61
−42.66
−42.64
−42.63
−42.56
−42.25
−42.17
−41.33
−41.01
−40.93
−40.67
−40.66
−40.66
−40.50
−40.08
−40.00
−39.54
50
50
637
50
50
52
1100
75
110
800
650
140
800
815
120
50
800
960
20
20
500
117
0–17 500
0–17 500
Modern
0–16 500
0–16 500
0–14 000
Modern
0–14 000
0–12 500
Modern
Modern
Modern
290–14 200
Modern
500–20 000
Modern
1800–4380
290–1260
Modern
Modern
4370–14 930
Modern
CTRF
CTRF
STEP
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CGSH
CTRF
TDFO
CTRF
CTRF
STEP
STEP
CTRF
STEP
Lake
Lake
Soil
Lake
Lake
Mire
Soil
Mire
Mire
Soil
Mire
Mire
Mire
Soil
Mire
Soil
Midden
Midden
Soil
Soil
Section
Soil
10
4
–
5
5
9
–
12
7
–
–
Clim. Past, 5, 725–767, 2009
9
–
16
–
6
3
–
–
9
–
Analyst
Site publications
Raw
Raw
Björck, S.
Markgraf, V.
7D
–
7D
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
Digi
Raw
Digi
Raw
Raw
Digi
Raw
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Raw
Heusser, C.
Markgraf, V.
Heusser, C.
Schäbitz, F
Schäbitz, F.
Heusser, C.
Ager, T. A.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Mancini, M. V.
Heusser, C.
Markgraf, V.
–
–
1C
–
–
4D
Raw
Raw
Digi
Graf, K.
Schäbitz, F.
Lumley, S.
1C
4C
–
2C
2C
1C
–
3C
2C
–
–
–
1C
–
4C
–
–
–
–
–
2C
–
2D
7D
–
7D
6D
–
–
–
–
–
–
–
–
–
1C
–
–
–
–
–
–
–
Digi
Digi
Raw
Digi
Digi
Digi
Raw
Digi
Digi
Raw
Raw
Raw
Raw
Raw
Raw
Raw
Raw
Raw
Raw
Raw
Raw
Raw
Bennet, K.
Bennet, K.
Schäbitz, F.
Bennet, K.
Bennet, K.
Heusser, C.
Schäbitz, F.
Heusser, C.
Heusser, C.
Schäbitz, F.
Moar, N. T.
Moar, N. T.
Markgraf, V.
Schäbitz, F.
Moreno, P. I.
Schäbitz, F.
Markgraf, V.
Markgraf, V.
Schäbitz, F.
Schäbitz, F.
Markgraf, V.
Schäbitz, F.
Björck et al. (1993)
Markgraf (1989, 1991,
1993)
Heusser et al. (1995)
Markgraf (1993)
Heusser et al. (1995)
Schäbitz (1991)
Schäbitz (1991)
Heusser et al. (1995)
Mercer and Ager (1983)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Mancini (1993)
Heusser et al. (1995)
Ashworth and Markgraf
(1989); Ashworth et al.
(1991)
Graf (1992)
Schäbitz (1994)
Lumley and Switsur
(1993)
Bennet et al. (2000)
Bennet et al. (2000)
Schäbitz (1994)
Bennet et al. (2000)
Bennet et al. (2000)
Heusser et al. (1995)
Schäbitz (1994)
Heusser et al. (1995)
Heusser et al. (1995)
Schäbitz (1994)
Godley and Moar (1973)
Godley and Moar (1973)
Markgraf (1983)
Schäbitz (1994)
Moreno (1994)
Schäbitz (1994)
Markgraf et al. (2009)
Markgraf et al. (2009)
Schäbitz (1994)
Schäbitz (1994)
Markgraf (1991)
Schäbitz (1994)
www.clim-past.net/5/725/2009/
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
735
Table 3. Continued.
Site
Country
Long.
Latitude
Alt.
Age range
Present biome
Sample
type
RC
DC at 6000
yr BP
DC at 18 000
yr BP
Data type
Pedro Luro
Origone
Gaviotas
Salina Anzotegui
Cueva Haichol
Arroyo Sauce Chico
Cerro La China
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
−62.53
−62.43
−63.65
−63.77
−70.66
−62.23
−58.64
−39.50
−39.08
−39.07
−39.06
−38.58
−38.07
−37.84
20
20
90
−5
1050
85
200
Modern
Modern
Modern
0–10 360
200–6890
Modern
0–10 500
STEP
STEP
TDFO
STEP
STEP
STEP
STEP
Soil
Soil
Soil
Playa
Cave
Soil
Soil
–
–
–
5
2
–
2
–
–
–
3D
1C
–
4C
–
–
–
–
–
–
–
Empalme Querandı́es
Veranada Pelan
Vaca Lauquen
Veranada Vulkanpickel
Salado
Salina 2
Serra do Rio Rastro
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Brazil
−60.65
−70.38
−71.08
−70.41
−69.75
−69.33
−49.55
−37.00
−36.88
−36.83
−36.68
−35.33
−32.25
−28.55
105
1860
1450
2800
3200
2000
1420
0–15 000
0–10 890
0–11 260
0–7790
20–4330
100–6510
800–11 180
STEP
CGSH
CTRF
CGSH
CGSH
CGSH
WTRF
Lake
Mire
Mire
Mire
Mire
Mire
Mire
8
3
3
1
2
2
3
2C
3C
1C
7D
–
1C
2C
Morro da Igreja
Brazil
−49.86
−28.18
1800
0–10 390
WTRF
Mire
2
Serra da Boa Vista
Brazil
−49.15
−27.70
1160
0–14 000
WTRF
Mire
4
Rano Kao
Chile
−109.43
−27.18
110
0–1360
TDFO
Lake
2
Rano Raraku Bore 3
Chile
−109.28
−27.16
75
0–35 260
TDFO
Lake
Rano Aroui
Chile
−109.40
−27.08
425
0–37 600
TDFO
Poço Grande
Brazil
−48.86
−26.41
10
0–4840
WTRF
Reserva Volta Velha
Brazil
−48.38
−26.04
0
Modern
WTRF
Atlantic
Brazil
−48.35
−25.95
200
Modern
WTRF
Colombo
Analyst
Site publications
Raw
Raw
Raw
Raw
Raw
Raw
Raw
Schäbitz, F.
Schäbitz, F.
Schäbitz, F.
Schäbitz, F.
Markgraf, V.
Prieto, A. R.
Prieto, A. R.
–
–
–
–
–
–
–
Raw
Raw
Raw
Raw
Raw
Raw
Raw
Prieto, A. R.
Schäbitz, F.
Markgraf, V.
Schäbitz, F.
Markgraf, V.
Markgraf, V.
Behling, H.
–
–
Raw
Behling, H.
2C
–
Raw
Behling, H.
–
–
Raw
Flenley, J.
10
4C
4C
Raw
Flenley, J.
Lake
11
2C
6C
Raw
Flenley, J.
Section
4
–
–
Raw
Behling, H.
Trap
–
–
–
Digi
Behling, H.
Trap
–
–
–
Raw
Behling, H.
Schäbitz (1994)
Schäbitz (1994)
Schäbitz (1994)
Schäbitz (1994)
Markgraf (1988)
Prieto (1996)
Prieto and Páez
(1989); Páez and
Prieto (1993)
Prieto (1996)
Schäbitz (1989)
Markgraf (1987)
Schäbitz (1989)
Markgraf (1983)
Markgraf (1983)
Behling
(1993);
Behling (1997a, b)
Behling
(1993);
Behling (1997a, b)
Behling
(1993);
Behling (1997a, b)
Flenley and King
(1984); Flenley et
al. (1991)
Flenley and King
(1984); Flenley et
al. (1991)
Flenley and King
(1984)
Behling
(1993);
Behling (1997a, b)
Behling et al.
(1997)
Behling et al.
(1997)
Behling et al.
(1997)
Behling (1997a)
Markgraf (1985)
Behling et al.
(1997)
Graf (1992)
Graf (1992)
Behling (2009)
Behling (2009)
Ledru et al.
(2001)
Behling (1997b)
Behling (2009)
Salgado-Labouriau
et al. (1998)
Behling (2009)
Behling (2009)
Ledru
(1992,
1993); Ledru et al.
(1994, 1996)
Graf (1992)
Graf (1992)
Behling
(1993);
Behling (1997a, b)
Graf (1992)
Salgado-Labouriau
et al. (1998)
Graf (1992)
Mourguiart et al.
(1995);
Argollo
and
Mourguiart
(2000)
Graf (1992)
Graf (1992)
Graf (1992)
Graf (1992)
Behling et al.
(2000)
Behling (2009)
Salgado-Labouriau
et al. (1998)
Behling (2009)
Graf (1992)
Graf (1992)
Graf (1992)
Mayle et al.
(2000)
Behling et al.
(2000)
Mayle et al.
(2000)
Hansen et al.
(1994)
Brazil
−49.23
−25.33
920
Modern
TSFO
Trap
–
–
–
Raw
Behling, H.
Brazil
Argentina
Brazil
−50.21
−65.75
−48.83
−24.66
−23.83
−23.83
1200
4000
800
0–12 480
0–9830
0–7500
WTRF
CGSH
WTRF
Mire
Mire
Lake
4
3
4
3C
2C
–
–
–
–
Raw
Raw
Raw
Behling, H.
Markgraf, V.
Behling, H.
Tumbre 2
Aguas Calientas
Botucatu
Curcuab
Lagoa da Caço
Chile
Chile
Brazil
Brazil
Brazil
−67.78
−67.42
−48.00
−48.00
−43.43
−23.31
−23.08
−23.00
−23.00
−22.97
3880
4210
700
700
5
241–7500
0–6400
Modern
Modern
3000–20 000
CGSH
CGSH
WTRF
WTRF
TDFO
Lake
Mire
Soil
Soil
Lake
3
1
–
–
14
2C
7D
–
–
1C
–
–
–
–
–
Raw
Raw
Raw
Raw
Raw
Graf, K.
Graf, K.
Behling, H.
Behling, H.
Behling, H.
Morro de Itapeva
Assis
Lagoa Santa
Brazil
Brazil
Brazil
−45.63
−50.50
−47.45
−22.78
−22.68
−22.36
1850
540
630
0–35 010
Modern
Modern
WTRF
TSFO
WTRF
Lake
Soil
River
9
–
–
4C
–
–
–
–
–
Raw
Raw
Digi
Behling, H.
Behling, H.
Parizzi, M. G.
Bauru
Brotas
Salitre
Brazil
Brazil
Brazil
−49.07
−48.08
−46.78
−22.32
−22.29
−19.00
570
700
1050
Modern
Modern
0–50 000
TSFO
WTRF
WTRF
Soil
Soil
Lake
–
–
14
–
–
1C
–
–
7C
Raw
Raw
Raw
Behling, H.
Behling, H.
Ledru, M.-P.
Ajata
Sajama
Lago do Pires
Chile
Bolivia
Brazil
−69.20
−68.88
−42.21
−18.25
−18.16
−17.95
4700
4250
390
0–1460
0–4400
0–9720
CGSH
CGSH
TSFO
Mire
Lake
Lake
1
9
7
–
–
1C
–
–
–
Raw
Raw
Raw
Graf, K.
Graf, K.
Behling, H.
Wasa Mayu
Crominia
Bolivia
Brazil
−65.91
−49.45
−17.54
−17.28
2720
200
1000–31 000
0–32 200
COMI
TDFO
Lake
Palm swamp
1
5
7D
2C
7D
1D
Raw
Digi
Mt. Blanco
Lake Huinãmimarca
Bolivia
Bolivia
−67.35
−69.00
−17.02
−16.50
4780
3765
1250–7500
0–25 000
CGSH
CSGH
Lake
Lake
7
48
1C
1C
–
3C
Raw
Digi
Graf, K.
SalgadoLabouriau, M. L.
Graf, K.
Mourguiart, P. H.
Cerro Calvario
Rio Kaluyo
Chacaltaya 1
Cumre Unduavi
Rio Jequitinhonha
Bolivia
Bolivia
Bolivia
Bolivia
Brazil
−68.50
−68.13
−68.13
−68.03
−38.92
−16.50
−16.43
−16.36
−16.33
−15.85
3950
4070
4750
4620
80
0–8360
130–9920
80–7400
0–9200
Modern
CGSH
CGSH
CGSH
CGSH
TDFO
Mire
Lake
Mire
Mire
River
4
3
1
6
–
1C
3C
1C
3C
–
–
–
–
–
–
Raw
Raw
Raw
Raw
Raw
Graf, K.
Graf, K.
Graf, K.
Graf, K.
Behling, H.
Brasilia 1
Aguads Emendadas
Brazil
Brazil
−47.66
−47.58
−15.59
−15.56
1030
200
Modern
0–28 000
TDFO
TDFO
Soil
Palm swamp
–
6
–
7C
–
7D
Raw
Digi
Cuiaba
Amarete
Cotapampa
Katantica
Laguna Chaplin
Brazil
Bolivia
Bolivia
Bolivia
Bolivia
−55.86
−68.98
−69.11
−69.18
−61.05
−15.35
−15.23
−15.21
−14.8
−14.50
350
4000
4450
4820
750
Modern
0–9160
0–9560
50–7720
0–40 000
TDFO
CGSH
CGSH
CGSH
TSFO
Soil
Mire
Mire
Mire
Lake
–
2
5
3
14
–
5D
2C
1C
2C
–
–
–
–
1D
Raw
Raw
Raw
Raw
Digi
Behling, H.
SalgadoLabouriau, M. L.
Behling, H.
Graf, K.
Graf, K.
Graf, K.
Mayle, F.
Rio de Contas
Brazil
−39.00
−14.28
80
Modern
TDFO
River
–
–
–
Raw
Behling, H.
Laguna Bella Vista
Bolivia
−61.56
−13.58
750
0–55 000
TSFO
Lake
15
2C
1D
Digi
Mayle, F.
Peru
−75.21
−11.78
4450
0–11 260
CGSH
Lake
4
4C
–
Raw
Hansen, B. C. S.
Serra Campos Gerais
Aguilar
Rio da Curuá
Laguna Jeronimo
www.clim-past.net/5/725/2009/
Clim. Past, 5, 725–767, 2009
736
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
Table 3. Continued.
Site
Country
Long.
Latitude
Alt.
Age range
Present biome
Sample
type
RC
DC at 6000
yr BP
DC at 18 000
yr BP
Data type
Laguna Pomacocha
Peru
−75.50
−11.75
4450
4100–10 220
CGSH
Lake
4
1C
–
Laguna Paca
Peru
−75.50
−11.71
3600
0–5410
CGSH
Lake
1
4D
Laguna Tuctua
Peru
−75.00
−11.66
4250
0–11 390
CGSH
Lake
4
2C
–
Laguna Milloc
Laguna Junin2
Peru
Peru
−76.35
−76.18
−11.56
−11.00
4325
4100
280–11 050
0–43 000
CGSH
CGSH
Lake
Lake
1
11
3C
6C
Laguna Huatacocha
Analyst
Site publications
Raw
Hansen, B. C. S.
Raw
Hansen, B. C. S.
Raw
Hansen, B. C. S.
–
1C
Raw
Raw
Graf, K.
Hansen, B. C. S.
Hansen et al.
(1994)
Hansen and Rodbell (1995)
Hansen et al.
(1994)
Graf (1992)
Hansen and Rodbell (1995)
Hansen and Rodbell (1995)
De Oliveira et al.
(1999)
De Oliveira et al.
(1999)
Behling et al.
(2000)
van der Hammen
and Absy (1994)
Behling et al.
(2000)
Behling et al.
(2000)
Behling et al.
(2000)
Behling et al.
(2000)
Behling et al.
(2000)
Behling et al.
(2000)
Behling et al.
(2000)
Behling et al.
(2000)
Absy et al.
(1991)
van der Hammen
and
González
(1960)
Behling et al.
(2000)
Behling et al.
(2000)
Colinvaux et al.
(1997)
Bush et al. (1990);
Bush and Colinvaux (1988); Colinvaux et al. (1988)
Bush et al. (1990)
Bush et al. (1990)
Bush et al. (1990)
Bush et al. (2000)
Bush et al. (2000)
Bush et al. (1990)
Bush et al. (1990)
Bush et al. (1990)
Behling et al.
(1997)
Behling (1996)
Behling et al.
(1999)
Behling et al.
(1999)
Bush et al. (1990)
Colinvaux et al.
(1988)
Graf (1989, 1992)
Bush et al. (1990)
Bush et al. (1990);
Colinvaux et al.
(1988)
Bush et al. (1990)
Bush et al. (1990)
Colinvaux et al.
(1988); Bush et al.
(1990)
Bush et al. (1990)
Bush et al. (1990)
Bush et al. (1990)
Colinvaux et al.
(1996); Colinvaux
et al. (2000)
De Oliveira (1992)
Behling
and
Hooghiemstra
(1999)
van der Hammen et
al. (1980)
Peru
−76.55
−10.76
4500
0–10 620
CGSH
Lake
5
2C
–
Raw
Hansen, B. C. S.
Rio São Francisco
Brazil
−43.00
−10.46
400
0–11 500
TDFO
River
6
1C
–
Digi
De Oliveira, P. E.
Saquinho
Brazil
−43.23
−10.44
480
0–11 000
TSFO
Mire
6
1C
–
Digi
De Oliveira, P. E.
Rio São Francisco
Brazil
−36.50
−10.26
80
Modern
TDFO
River
–
–
–
Raw
Behling, H.
Katira
Brazil
−63.00
−9.00
750
0–60 000
TDFO
Lake
4
7D
1D
Digi
Campina Grande I
Brazil
−35.75
−7.23
70
Modern
TSFO
Soil
–
–
–
Raw
van der Hammen,
T.
Behling, H.
Lagoa Grande
Brazil
−47.45
−7.08
75
Modern
TDFO
Lake
–
–
–
Raw
Behling, H.
Picos
Brazil
−41.40
−7.06
70
Modern
TDFO
Soil
–
1D
1D
Raw
Harbele, S.
Lago Bolim
Brazil
−35.18
−6.04
90
Moden
TDFO
Lake
–
–
–
Raw
Behling, H.
Rio Protengi
Brazil
−35.25
−5.78
80
Modern
TDFO
River
–
–
–
Raw
Behling, H.
Mirim
Brazil
−35.30
−5.68
70
Modern
TDFO
Soil
–
1D
7D
Raw
Ledru, M.-P.
Rio Mirim
Brazil
−35.40
−5.64
70
Modern
TDFO
River
–
–
–
Raw
Behling, H.
Rio (unclear)
Brazil
−38.00
−5.50
50
Modern
TDFO
River
–
–
–
Raw
Behling, H.
Carajas
Brazil
−48.00
−5.00
150
0–20 000
TSFO
Lake
8
2C
6C
Digi
Absy, M. L.
Colombia
−74.18
−4.75
2560
0–>35 000
COMI
Lake
4
4C
7D
Raw
van der Hammen,
T.
Brazil
−37.76
−4.55
50
Modern
TDFO
River
–
–
–
Raw
Behling, H.
Rio Jaguaribe I
Brazil
−37.75
−4.43
50
Modern
TDFO
River
–
–
–
Raw
Behling, H.
Lake Surucucho
Ecuador
−78.95
−3.75
970
0–12 000
WTRF
Lake
9
4C
–
Digi
Colinvaux, P.
Ayauch
Ecuador
−78.13
−2.09
550
0–7500
WTRF
Lake
4
2C
–
Digi
Bush, M.
Llaviucu
Kumpack
Indanza
Comprida
Geral
Puyo Bog
Rum Tum
Yambo
Lago Crispim
Ecuador
Ecuador
Ecuador
Brazil
Brazil
Ecuador
Ecuador
Ecuador
Brazil
−79.43
−78.51
−78.83
−53.15
−53.00
−79.06
−79.03
−79.03
−48.00
−1.83
−1.53
−1.53
−1.5
−1.5
−1.43
−1.13
−1.03
−0.8
3120
700
2100
130
130
953
2392
2600
0
Modern
Modern
Modern
0–7200
0–6000
Modern
Modern
Modern
0–9000
CTRF
WTRF
CTRF
TRFO
TRFO
WTFO
CTRF
CTRF
TRFO
Lake
Lake
Lake
Lake
Lake
Lake
Lake
Lake
Lake
–
–
–
5
2
–
–
–
4
–
–
–
1C
1C
–
–
–
1C
–
–
–
–
–
–
–
–
–
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Digi
Raw
Bush, M.
Bush, M.
Bush, M.
Bush, M.
Bush, M.
Bush, M.
Bush, M.
Bush, M.
Behling, H.
Lagoa da Curuça2
Mariñame-II
Brazil
Colombia
−47.85
−72.03
−0.76
−0.66
35
160
0–9440
0–5000
TRFO
TRFO
Lake
Lake
4
5
2C
1C
–
–
Raw
Raw
Behling, H.
Behling, H.
Monica-1
Ciudad Universitaria X
Rio Jaguaribe II
Colombia
−72.50
−0.60
160
0–12 000
TRFO
Lake
3
2C
–
Raw
Behling, H.
Añangucocha
Limoncocha
Ecuador
Ecuador
−77.03
−76.66
−0.53
−0.38
280
230
Modern
0–1300
TRFO
TRFO
Lake
Lake
–
2
–
–
–
–
Digi
Digi
Bush, M.
Colinvaux, P.
Cayambe
Cuyabeno
Lago Agrio
Ecuador
Ecuador
Ecuador
−78.03
−77.01
−77.03
−0.03
0
0.03
4350
280
330
0–5200
Modern
Modern
CGSH
TRFO
TRFO
Mire
Lake
Lake
6
–
–
4D
–
–
–
–
–
Raw
Digi
Digi
Graf, K.
Bush, M.
Bush, M.
San Marcos
Santa Cecilia
Lake Santa Cecilia
Ecuador
Ecuador
Ecuador
−79.03
−77.03
−77.02
0.03
0.04
0.06
3400
330
330
Modern
Modern
0-1000
CGSH
TRFO
TRFO
Mire
Lake
Lake
–
–
2
–
–
–
–
–
–
Digi
Digi
Digi
Bush, M.
Bush, M.
Colinvaux, P.
Cunro
Mera
Yaguara cocha
La Pata
Ecuador
Ecuador
Ecuador
Brazil
-79.03
−76.92
−79.03
−66.66
0.08
0.11
0.13
0.25
2800
1100
2210
300
Modern
Modern
Modern
0–45 000
CTRF
WTRF
CTRF
TRFO
Lake
Lake
Lake
Lake
–
–
–
12
–
–
–
2C
–
–
–
1D
Digi
Digi
Raw
Digi
Bush, M.
Bush, M.
Bush, M.
Colinvaux, P.
Lagoa das Patas
Piusbi
Brazil
Colombia
−66.68
−77.89
0.26
1.66
300
200
0–42 210
0–10 400
TFFO
TRFO
Lake
Lake
16
3
1D
1C
1C
–
Raw
Raw
De Oliveira, P. E.
Behling, H.
Pitalito
Colombia
−76.50
1.75
1300
0–7350
WEFO
Mire
6
6D
–
Raw
Bakker, J. G. M.
Clim. Past, 5, 725–767, 2009
www.clim-past.net/5/725/2009/
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
737
Table 3. Continued.
Site
Country
Long.
Latitude
Alt.
Age range
Present biome
Sample
type
RC
DC at 6000
yr BP
DC at 18 000
yr BP
Data type
Analyst
Site publications
Piagua
Colombia
−76.50
2.30
1700
0–14 000
WEFO
Lake
7
7D
–
Raw
Wille, M.
Raw
Behling, H.
Wille et al. (2001);
van der Hammen et al.
(1980)
Behling et al. (1999);
van der Hammen et al.
(1980)
van der Hammen et al.
(1980)
Wille et al. (1999)
Behling et al. (1999)
Wijmstra and van der
Hammen (1966)
Melief (1985)
Melief (1985)
Melief (1985)
Melief (1985)
Melief (1985)
Behling and Hooghiemstra (1999)
Melief (1985)
Behling and Hooghiemstra (1999)
Melief (1985)
Melief (1985)
Wille et al. (2001);
van der Hammen et al.
(1980)
Kuhry (1988); van der
Hammen and González
(1960)
van der Hammen et al.
(1980)
Behling and Hooghiemstra (1998)
van der Hammen et al.
(1980)
van der Hammen (1974)
Pantano de Genagra
Colombia
−76.50
2.50
1750
0–9000
WEFO
Mire
7
4C
–
Rio Timbio
Colombia
−76.50
2.50
1750
0–14 000
WEFO
Lake
6
2C
–
Raw
Wille, M.
El Caimito
Loma Linda
Lago Agua Sucia
Colombia
Colombia
Colombia
−76.60
−73.35
−73.54
2.53
3.22
3.46
50
310
260
0–4500
0–8720
0–15 340
TRFO
TDFO
TDFO
Lake
Lake
Lake
4
8
4
–
1C
7D
–
–
–
Raw
Raw
Raw
Wille, M.
Behling, H.
Wijmstra, T. A.
El Gobernador
La Guitarra
La Primavera
Corazón Partido
El Trinagulo
Carimagua
Colombia
Colombia
Colombia
Colombia
Colombia
Colombia
−75.00
−74.28
−74.13
−74.25
−74.25
−74.14
3.95
4.00
4.00
4.00
4.00
4.04
3815
3450
3525
4100
4100
180
0–10 050
0–15 300
0–11 200
Modern
Modern
0–8270
CTRF
COMI
CGSH
CGSH
CGSH
TDFO
Mire
Mire
Mire
Mire
Mire
Lake
2
3
6
–
–
6
2C
4C
1C
–
–
2C
–
–
–
–
–
–
Raw
Raw
Raw
Raw
Raw
Raw
Melief, A. B. M.
Melief, A. B. M.
Melief, A. B. M.
Melief, A. B. M.
Melief, A. B. M.
Behling, H.
La Rabona
El Piñal
Colombia
Colombia
−74.25
−70.40
4.05
4.09
4000
185
0–5100
0–19 000
CGSH
TDFO
Mire
Lake
1
8
4D
4C
–
2D
Raw
Raw
Melief, A. B. M.
Behling, H.
Alsacia
Andabobos
Ubaqué
Colombia
Colombia
Colombia
−74.11
−74.15
−73.55
4.09
4.09
4.33
3100
3570
2000
0–13 700
0–15 000
Modern
COMI
CGSH
WEFO
Mire
Mire
Lake
3
2
–
6D
7D
–
–
–
–
Raw
Raw
Raw
Melief, A. B. M.
Melief, A. B. M.
Jean-Jarcob, K.
de la América
Colombia
−74.00
4.33
3550
0–9000
CGSH
Mire
1
1D
–
Raw
Kuhry, P.
Turbera de Calostros
Colombia
−73.48
4.41
3730
Modern
CGSH
Soil
1
–
–
Raw
Salomons, J. B.
Laguna Angel
Colombia
−70.54
4.45
205
0–10 026
TDFO
Lake
8
2C
–
Raw
Behling, H.
Libano
Colombia
−75.50
4.50
1820
0–14 000
WEFO
Soil
1
7D
–
Raw
Salomons, J. B.
de Pedro Palo III
Colombia
−74.41
4.50
2000
0–5500
COMI
Lake
2
7D
-
Raw
Sardinas
Colombia
−69.45
4.95
180
0–11 600
TDFO
Lake
6
2C
–
Raw
van der Hammen,
T.
van der Hammen,
T.
van der Hammen,
T.
Behling, H.
Herrera
Colombia
−73.91
5.00
2000
0–20 000
COMI
Lake
3
4D
–
Raw
van Geel, B.
Agua Blanca
Colombia
−74.45
5.0
3250
0–46 000
COMI
Mire
2
6D
7D
Raw
Kuhry, P.
Paramo Palacio
Colombia
−73.88
4.76
3550
0–5500
CGSH
Mire
4
5D
–
Raw
Greja
Colombia
−73.70
4.86
4000
0–12 000
CGSH
Lake
2
3C
–
Raw
El Abra II
Colombia
−73.96
5.02
2570
0–11 000
COMI
Cave
1
7D
–
Raw
Paramo de Peña Negra
ODP site 932
Colombia
Brazil
−74.09
−47.03
5.09
5.18
3625
0
0–12 500
0–45 000
CGSH
TRFO
Mire
Fan
10
2
2C
2C
–
1D
Raw
Raw
Schreve-Brinkman,
E. J.
Kuhry, P.
Haberle, S.
Paramo de Laguna Verde
Fúquene II
Colombia
Colombia
−74.00
−73.87
5.25
5.50
3625
2580
0–5500
0–25 000
CGSH
COMI
Mire
Lake
2
2
4D
7D
–
7C
Raw
Raw
Kuhry, P.
van Geel, B.
Jotaordó
Cienaga del Visitador
Colombia
Colombia
−76.66
−72.83
5.66
6.13
50
3300
0–4200
0–12 000
TRFO
COMI
Lake
Mire
7
2
–
7D
–
–
Raw
Raw
Berrio, J. C. B.
van der Hammen,
T.
van der Hammen,
T.
van der Hammen,
T.
van der Hammen,
T.
Bush, M.
Bobos
Colombia
−72.85
6.13
3800
0–5000
CGSH
Lake
4
6D
–
Raw
Ciega I
Colombia
−72.31
6.50
3510
0–2000
COMI
Lake
1
–
–
Raw
Valle de Lagunillas
Colombia
−72.34
6.50
3880
0–7100
CGSH
Lake
8
7D
–
Raw
Panama
−77.58
7.68
1000
Modern
TSFO
Pollster
–
–
–
Raw
Cana, Darien
Cana Swamp
Panama
−77.59
7.74
500
0-4600
TRFO
Swamp
5
–
–
Digi
Bush, M.
Wodehouse Swamp
Panama
−77.58
7.75
500
0–4200
TRFO
Swamp
1
–
–
Digi
Bush, M.
Costa Rica
−82.00
8.00
2310
0–80 000
CTRF
Lake
3
2C
7D
Digi
Islebe, G.
La Chonta
www.clim-past.net/5/725/2009/
van der Hammen and
González (1960)
van der Hammen (1962)
Behling and Hooghiemstra (1998)
van Geel and van der
Hammen (1973)
Graf (1992);
Kuhry
(1988b); Kuhry et al.
(1983)
Schreve-Brinkman
(1978)
Kuhry et al. (1983)
Haberle and Maslin
(1999)
Kuhry et al. (1983)
van Geel and van der
Hammen (1973)
Berrio et al. (2000)
van der Hammen and
González (1965)
van der Hammen (1962)
van der Hammen et al.
(1980)
van der Hammen et al.
(1980)
Bush (1995); Bush and
Rivera (1998)
Bush and Colinvaux
(1994)
Bush and Colinvaux
(1994)
Hooghiemstra et al.
(1992);
Islebe and
Hooghiemstra (1997);
Islebe et al. (1995a, b)
Clim. Past, 5, 725–767, 2009
738
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
Table 3. Continued.
Site
Country
Long.
Latitude
Alt.
Age range
Present biome
Sample
type
RC
DC at 6000
yr BP
DC at 18 000
yr BP
Data type
Analyst
Site publications
El Valle
Panama
−79.78
8.43
500
0–20 000
TSFO
Lake
5
–
2C
Digi
Bush, M.
La Yeguada,
Panama
−80.78
8.43
650
0–14 000
TSFO
Lake
11
1C
–
Digi
Bush, M.
Cerro Campana
Panama
−79.93
8.63
800
Modern
WTFO
Pollster
–
–
–
Raw
Bush, M.
Quebrada Nelson
Bush (1995); Bush and
Rivera (1998); Piperno et
al. (1991a, b)
Bush (1995); Bush et al.
(1992); Bush and Rivera
(1998); Piperno et al.
(1991a, b)
Bush (1995); Bush and
Rivera (1998)
Bush (1995); Bush and
Rivera (1998)
Behling (2000)
Rull et al. (1987)
Rull et al. (1987)
Bush (1995); Bush and
Rivera (1998)
Kuhry (1988a); Melief
(1985)
Bush (1995); Bush and
Rivera (1998)
Bush (1995); Bush and
Rivera (1998)
Bush (1995); Bush and
Rivera (1998)
Salgado-Labouriau
(1988, 1991)
Leyden et al. (1995)
Bush (1995); Bush and
Rivera (1998)
Bush (1995); Bush and
Rivera (1998)
Rull et al. (1987)
Panama
−82.31
8.66
1130
Modern
WTRF
Pollster
–
–
–
Raw
Bush, M.
Laguna Volcán
Valle Laguna Negra
Valle Laguna Victoria
Horsefly Ridge
Panama
Venezuela
Venezuela
Panama
−82.75
−70.76
−70.79
−82.24
8.75
8.79
8.80
8.83
1500
3450
3250
1150
0–2860
0–3350
0–12 210
Modern
WTRF
CGSH
CGSH
WTRF
Lake
Lake
Lake
Pollster
4
1
4
–
–
–
4C
–
–
–
–
–
Digi
Raw
Raw
Raw
Behling, H.
Graf, K.
Graf, K.
Bush, M.
El Bosque
Colombia
−75.45
8.85
3650
0–4700
CGSH
Mire
4
–
–
Raw
Melief, A. B. M.
Volcan Baru
Panama
−82.52
8.85
2600
Modern
CTRF
Pollster
–
–
–
Raw
Bush, M.
Finca Lerida
Panama
−82.45
8.87
1630
Modern
WTRF
Pollster
–
–
–
Raw
Bush, M.
Volcan Irazu
Panama
−82.52
8.88
2300
Modern
CTRF
Pollster
–
–
–
Raw
Bush, M.
Venezuela
−70.85
8.91
3290
310–11 470
CGSH
Mire
3
4C
–
Raw
Panama
Ocelot Pond
Paramo de Miranda
Panama
Panama
−80.87
−79.59
9.00
9.12
100
20
0–7200
Modern
TSRO
TSFO
Lake
Pollster
8
–
1C
–
–
–
Raw
Raw
SalgadoLabouriau, M. L.
Leyden, B. W.
Bush, M.
Soberania
Panama
−79.66
9.13
20
Modern
TSFO
Pollster
–
–
–
Raw
Bush, M.
Paramo Piedras Blancas
Pipeline Rd
Barro Colorado Island
Venezuela
−70.83
9.16
4080
0–1340
CGSH
Mire
2
–
–
Raw
Panama
−79.66
9.33
40
Modern
TSFO
Pollster
–
–
–
Raw
SalgadoLabouriau, M. L.
Bush, M.
Panama
−79.75
9.35
50
Modern
TSFO
Pollster
–
–
–
Raw
Bush, M.
Lago Chirripó
Costa Rica
−83.48
9.48
3520
Modern
CTRF
Lake
–
–
–
Digi
Horn, S. P.
Talamancas
Costa Rica
−83.72
9.5
2500
Modern
CTRF
Pollster
–
–
–
Raw
Bush, M.
Lago de las Morrenas
Bog 70
Costa Rica
Costa Rica
−83.49
−83.85
9.50
9.61
3480
2670
0–10 000
Modern
CTRF
CTRF
Lake
Bog
6
–
2C
–
–
–
Digi
Digi
Horn, S. P.
Horn, S. P.
Tres de Junio
Costa Rica
−83.87
9.62
2670
Modern
CTRF
Bog
–
–
–
Digi
Horn, S. P.
Bog 68
Costa Rica
−83.85
9.64
2670
Modern
CTRF
Bog
–
–
–
Digi
Horn, S. P.
Asuncion
Costa Rica
−83.75
9.64
3340
Modern
CTRF
Lake
–
–
–
Digi
Horn, S. P.
Carara Biological Reserve
Costa Rica
−84.62
9.73
0
Modern
TSFO
Pollster
–
–
–
Raw
Bush, M.
Quebrador
Costa Rica
−83.84
9.74
3040
Modern
CTRF
Lake
–
–
–
Digi
Horn, S. P.
Cataracta, Carara 1
Costa Rica
−84.63
9.83
270
Modern
TSFO
Pollster
–
–
–
Raw
Bush, M.
Cataracta, Carara 2
Costa Rica
−84.63
9.85
270
Modern
TSFO
Pollster
–
–
–
Raw
Bush, M.
Carara
Costa Rica
−84.60
9.88
35
Modern
TSFO
Lake
–
–
–
Digi
Horn, S. P.
Laguna Bonilla
Costa Rica
−83.61
9.99
380
Modern
TSFO
Lake
–
–
–
Digi
Horn, S. P.
Laguna Barva
Costa Rica
−84.11
10.14
2840
Modern
CTRF
Lake
–
–
–
Digi
Horn, S. P.
Laguna Palmita
Costa Rica
−84.95
10.18
60
Modern
TDFO
Soil
–
–
–
Digi
Horn, S. P.
Laguna Botos
Costa Rica
−84.18
10.18
2600
Modern
CTRF
Lake
–
–
–
Digi
Horn, S. P.
Bosque Alegre
Costa Rica
−84.21
10.21
740
Modern
WTRF
Lake
–
–
–
Digi
Horn, S. P.
Laguna González
Costa Rica
−84.45
10.25
710
Modern
WTRF
Lake
–
–
–
Digi
Horn, S. P.
Laguna Congo
Costa Rica
−84.29
10.27
740
Modern
WTRF
Lake
–
–
–
Digi
Horn, S. P.
Laguna Hule
Costa Rica
−84.19
10.27
740
Modern
WTRF
Lake
–
–
–
Digi
Horn, S. P.
Laguna Marı́a Aguilar
Costa Rica
−84.18
10.27
770
Modern
WTRF
Lake
–
–
–
Digi
Horn, S. P.
Brauillo Carillo, Heredia
Costa Rica
−83.94
10.3
630
Modern
WTRF
Pollster
–
–
–
Raw
Bush, M.
Monteverde, Heredia
Costa Rica
−84.8
10.3
1500
Modern
WTRF
Pollster
–
–
–
Raw
Bush, M.
Volcan Poas
Costa Rica
−84.19
10.3
2580
Modern
CTRF
Pollster
–
–
–
Raw
Bush, M.
Lake Valencia
Venezuela
−67.75
10.32
403
400–13 000
STEP
Lake
28
1C
–
Raw
Leyden, B. W.
Laguna Rı́o Cuarto
Costa Rica
−84.18
10.34
380
Modern
WTRF
Lake
–
–
–
Digi
Horn, S. P.
Clim. Past, 5, 725–767, 2009
Bush (1995); Bush and
Rivera (1998)
Bush (1995); Bush and
Rivera (1998)
Rodgers
and
Horn
(1996)
Bush (1995); Bush and
Rivera (1998)
Horn (1993)
Rodgers
and
Horn
(1996)
Rodgers
and
Horn
(1996)
Rodgers
and
Horn
(1996)
Rodgers
and
Horn
(1996)
Bush (1995); Bush and
Rivera (1998)
Rodgers
and
Horn
(1996)
Bush (1995); Bush and
Rivera (1998)
Bush (1995); Bush and
Rivera (1998)
Rodgers
and
Horn
(1996)
Rodgers
and
Horn
(1996)
Rodgers
and
Horn
(1996)
Rodgers
and
Horn
(1996)
Rodgers
and
Horn
(1996)
Rodgers
and
Horn
(1996)
Rodgers
and
Horn
(1996)
Rodgers
and
Horn
(1996)
Rodgers
and
Horn
(1996)
Rodgers
and
Horn
(1996)
Bush (1995); Bush and
Rivera (1998)
Bush (1995); Bush and
Rivera (1998)
Bush (1995); Bush and
Rivera (1998)
Bradbury et al. (1981);
Leyden (1985)
Rodgers
and
Horn
(1996)
www.clim-past.net/5/725/2009/
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
739
Table 3. Continued.
Site
Country
Long.
Latitude
Alt.
Age range
Present biome
Sample
type
RC
DC at 6000
yr BP
DC at 18 000
yr BP
Data type
Analyst
Site publications
La Selva, Heredia
Costa Rica
−84.00
10.43
80
Modern
TSFO
Pollster
–
–
–
Raw
Bush, M.
Cantarrana Swamp
Costa Rica
−84.00
10.45
36
Modern
TRFO
Swamp
–
–
–
Digi
Horn, S. P.
La Pacifica, Guanacaste
Costa Rica
−85.11
10.45
110
Modern
TSFO
Pollster
–
–
–
Raw
Bush, M.
Sendro Sedro Swamp
Costa Rica
−84.00
10.46
40
Modern
TRFO
Swamp
–
–
–
Digi
Horn, S. P.
Laguna La Palma
Costa Rica
−84.73
10.49
570
Modern
WTRF
Lake
–
–
–
Digi
Horn, S. P.
Laguna Cedeño
Costa Rica
−84.71
10.49
610
Modern
WTRF
Lake
–
–
–
Digi
Horn, S. P
Tortuguero
Costa Rica
−83.53
10.53
0
Modern
TSFO
Pollster
–
–
–
Raw
Bush, M.
Santa Rosa2
Costa Rica
−85.64
10.83
0
Modern
TSFO
Soil
–
–
–
Digi
Horn, S. P.
Santa Rosa1
Costa Rica
−85.66
10.84
0
Modern
TRFO
Soil
–
–
–
Digi
Horn, S. P.
Boca de Lopez
Colombia
−75.36
10.85
0
0–4000
TRFO
Coastal
5
–
–
Raw
Cafetal, Guanacaste
Costa Rica
−85.65
10.85
300
Modern
TSFO
Pollster
–
–
–
Raw
van der Hammen,
T.
Bush, M.
Santa Rosa3
Costa Rica
−85.62
10.86
280
Modern
TSFO
Soil
–
–
–
Digi
Horn, S. P.
Santa Rosa4
Costa Rica
−85.62
10.86
280
Modern
TSFO
Soil
–
–
–
Digi
Horn, S. P.
Escondido
Costa Rica
−85.61
10.87
280
Modern
TSFO
Lake
–
–
–
Digi
Horn, S. P.
Volcán Cacao
Costa Rica
−85.47
10.92
1000
Modern
WTRF
Soil
–
–
–
Digi
Horn, S. P.
Sierra de Cuchumatanes5
Guatemala
−91.00
15.75
2800
Modern
WAMF
Polster
–
–
–
Digi
Islebe, G.
Sierra de Cuchumatanes4
Guatemala
−91.25
15.75
3000
Modern
CTRF
Polster
–
–
–
Digi
Islebe, G.
Sierra de Cuchumatanes3
Guatemala
−91.5
15.75
3400
Modern
CTRF
Polster
–
–
–
Digi
Islebe, G.
Sierra de Cuchumatanes2
Guatemala
−91.75
15.75
3600
Modern
WAMF
Polster
–
–
–
Digi
Islebe, G.
Sierra de Cuchumatanes1
Guatemala
−92.00
15.75
4200
Modern
CGSH
Polster
–
–
–
Digi
Islebe, G.
Lago Quexil
Guatemala
−89.88
16.92
110
0–27 500
TSFO
Lake
4
7D
–
Raw
Leyden, B. W.
Lake Peten-Itza
Guatemala
−90.00
17.25
200
0–9000
TDFO
Lake
7
2C
–
Digi
Islebe, G.
Lago Catemaco
Mexico
−95.00
18.66
340
0–2230
TRFO
Lake
5
2C
–
Raw
Byrne, A. R.
Zempoala
Quila
Lake Texcoco
Mexico
Mexico
Mexico
−99.30
−99.20
−99.12
19.20
19.30
19.44
3100
2800
2330
0–4600
0–10 000
0–35 000
WAMF
WAMF
WAMF
Lake
Lake
Lake
5
4
7
–
3C
3C
–
–
4C
Raw
Raw
Digi
Almeida, L.
Almeida, L.
Lozano-Garcı́a, S.
Chalco Lake
Mexico
−99.00
19.50
2240
8000–27 500
WAMF
Lake
8
3C
1C
Raw
Lozano-Garcia,
M. S.
Lake Pátzcuaro
Mexico
−101.58
19.58
2044
20–44 100
WAMF
Lake
24
2C
6C
Raw
Watts, W. A.
San Jose Chulchaca
Mexico
−90.13
20.86
1
0–7300
TDFO
Lake
8
2C
–
Raw
Leyden, B. W.
Lake Coba
Mexico
−87.55
20.86
100
5880–19 230
WAMF
Playa
8
7D
2C
Raw
Leyden, B. W.
Bush (1995); Bush
and Rivera (1998)
Rodgers and Horn
(1996)
Bush (1995); Bush
and Rivera (1998)
Rodgers and Horn
(1996)
Rodgers and Horn
(1996)
Rodgers and Horn
(1996)
Bush (1995); Bush
and Rivera (1998)
Rodgers and Horn
(1996)
Rodgers and Horn
(1996)
Behling et al.
(1999)
Bush (1995); Bush
and Rivera (1998)
Rodgers and Horn
(1996)
Rodgers and Horn
(1996)
Rodgers and Horn
(1996)
Rodgers and Horn
(1996)
Islebe
and
Hooghiemstra
(1995)
Islebe
and
Hooghiemstra
(1995)
Islebe
and
Hooghiemstra
(1995)
Islebe
and
Hooghiemstra
(1995)
Islebe
and
Hooghiemstra
(1995)
Leyden
(1984);
Leyden
et
al.
(1993, 1994)
Islebe et al.
(1996)
Byrne and Horn
(1989)
Almeida (1997)
Almeida (1997)
Lozano-Garcı́a and
Ortega-Guerrero
(2009)
Lozano-Garcia and
Ortega-Guerrero
(1994);
LozanoGarcia
et
al.
(1993);
OrtegaGuerrero (1992)
Saporito (1975),
Watts and Bradbuy
(1982)
Leyden et al.
(1995)
Leyden et al.
(1998)
that have been used to unravel past vegetation changes in
Latin America with ecosystem reconstructions now existing from all major vegetation types over the late Quaternary period. The Latin American Pollen Database (LAPD)
(www.ncdc.noaa.gov/paleo/lapd.html) is an online resource
used to collate these data and facilitated the systematic interoperation presented here; indeed the majority of the pollen
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data used here are available through the LAPD. Additional
data were obtained from researchers working in Latin America; all active palynolgists being given the opportunity to
contribute data not currently lodged in the LAPD. Indeed,
data from a number of sites in Argentina, Brazil, Costa Rica,
México and Panama were made available specifically for this
work. The majority of data from Colombia were prepared for
Clim. Past, 5, 725–767, 2009
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this analysis directly from the original count sheets and are
in preparation for uploading to the LAPD.
The majority of the data used in our analysis are complete
raw pollen counts, this permitted all pollen taxa recorded by
the original analyst to be allocated to PFTs and allowed the
integrity of the data to be maintained throughout the analysis. Application of raw pollen data in other regions has been
shown to help in differentiating between biomes (Tarasov et
al., 1998b). However, numerous pollen records are either
not submitted to the LAPD, or, were not made available for
this analysis. Rather than omitting these data, the pollen
counts were digitised from published pollen diagrams (Table 3): digitising such data provides a spatially more complete reconstruction than available from presented archived
data. The process of digitisation involved either back calculation of the pollen counts if information on the pollen
sum was present; if the pollen sum was not available the
pollen percentage diagram was used as a count of 100 and
values for the pollen taxa were abstracted at the time intervals used for our analysis. This scenario of combining
data from different formats comes with a number of caveats
that can have bearing on the results, and their interpretation
(Marchant and Hooghiemstra, 2001). Firstly, the sub-set of
pollen taxa in a count used to construct published pollen diagrams, and pollen sums that comprise it, often result from
the bias of individual researchers’, particularly on what are
the reliable indicator taxa for a particular area and range of
different vegetation types under investigation. This issue is
particularly crucial in Latin America where the large numbers of pollen taxa encountered in the original counting are
rarely depicted on published pollen diagrams. Furthermore,
the level of identification achieved within pollen analysis, to
a generic or family level, commonly comprises species that
can be found in a range of different vegetation types, ecologies and growth forms (Marchant et al., 2002c). The majority
of the samples for the biomisation presented here are derived
from sites close to the Andean spine. Primarily, this concentration reflects the sensitive response of the vegetation to climate change on the steep altitudinal gradients (Marchant et
al., 2001b); the area forming an ideal location for palaeoecological research. Additionally, the comparative lack of data
from the lowlands is fuelled by problems of access, suitable
sites and strong river dynamics that commonly result in sedimentary hiatuses (Ledru, 1998). Because of the steep environmental gradients associated with 7000 m of altitudinal
change found along the Andes this spatial bias did not reduce
the number of biomes we were able to reconstruct, (Fig. 4).
However, the concentration of sites did result in numerous
assignments being mapped overlapping each other and hence
the changes between the different time periods difficult to
detect. To rectify this problem in addition to presenting the
results on the traditional biome dot maps (Figs. 7, 8 and 9)
results are presented for all sites in a table (Table 6) that has
been ordered by altitude so one can see which biome has
been reconstructed for each site, how this compares to the
Clim. Past, 5, 725–767, 2009
potential vegetation and how it changes at each time period.
Uncalibrated radiocarbon dates available from the original
stratigraphic analysis were used to select samples representing the time period used here. On a site-by-site basis, a linear
age-depth model was applied to the pollen data. The validity
of this model was assessed at each site taking into account
sedimentary hiatuses and dating problems such as age reversals and dates with large standard errors; a summary of this
dating control is provided in Table 1 following the COHMAP
scheme (Webb, 1995; Yu and Harrison, 1999). Multiple samples (≤3) were selected when more than one sample fell
within the age range allowed for each time period. These
data were compiled, to produce a site vs taxa matrix that
was then checked to standardise nomenclature, e.g., the combined file contained many synonyms such as Gramineae and
Poaceae, and Mysine and Rapanea. Synonymous taxa were
combined using the nomenclature of Kewensis (1997) and
the International Plant Names Index (IPNI) (2008). Aquatic
and non-tree fern taxa were removed from the matrix as they
commonly reflect local hydrological conditions rather than
local climate envelope. Marker additions and exotic spikes
such as Lycopodium were also removed.
A total of 381 samples from 287 locations derived from
core tops (<500 14 C yr BP), surface samples, pollen traps
and moss polsters comprise the modern data set (Table 3).
For the time period 6000±500 14 C yr BP, 255 samples derived from 127 pollen records comprise the data set (Table 3).
For the time period 18 000±1000 14 C yr BP, 61 samples derived from 34 pollen records comprise the data set (Table 3).
The data sets to undergo analysis comprised 515 pollen taxa
for the modern calibration, 493 for the 6000 14 C yr BP reconstruction and 232 for the 18 000 14 C yr BP reconstruction.
The taxonomic diversity of the Neotropical phytogeographical realm is greater than Africa [364] (Jolly et al., 1998a),
Europe [41] (Prentice et al., 1996b), Russia and Mongolia
[98] (Tarasov et al., 1998a) and China [68] (Yu et al., 1998),
however this number will be biased by sampling intensity
and density and some of these previous applications only focuses on the numerically important taxa.
2.2
Biomisation
Prentice et al. (1996a) and Prentice and Webb (1998) have
documented the steps involved in the biomisation technique.
First, a conceptual framework for PFTs and biomes in Latin
American vegetation was developed by investigating the relationship between potential biomes and three environmental gradients. The environmental gradients considered were
moisture availability (α: Priestley-Taylor coefficient of plant
available moisture), temperature (MTCO: mean temperature
of the coldest month) and seasonal warmth (GDD: growing
degree-days). Similarly to the biomes, but at a finer ecological resolution, the spatial distribution of PFTs is determined
by environmental controls on plant growth form and ecological tolerance (Woodward, 1987). In Latin America the
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R. Marchant et al.: Pollen-based biome reconstructions for Latin America
dominant environmental gradients are temperature, primarily associated with altitude, moisture availability and seasonality. PFT definitions were modified from the classification
originally developed for the BIOME 1 model (Prentice et al.,
1992, 1996a, b) taking into account schemes developed for
other regions, particularly those that abut the Latin American region or contain similar floristic elements (Jolly et al.,
1998a; Pickett et al., 2004; Takahara et al., 2001 Elenga et
al., 2000; Thompson and Anderson, 2000; Yu et al., 2000).
Five main groups of PFT were distinguished: these containing tropical (non-frost tolerant), coniferous (needle-leaved),
temperate (frost tolerant), xerophytic (drought tolerant), and
frost and drought tolerant taxa (Table 1, Fig. 6). This latter group is present in cold dry conditions of southern South
America and the high Andes. A sixth “miscellaneous” group
represents various life forms with restricted diagnostic value.
The Latin American flora was divided into 25 PFTs (Table 1) that, although being of ecologically distinct, can be
multiply assigned to the biomes (Table 2). The classification is based on the original scheme devised for the Biome1 vegetation model (Prentice et al., 1992) and modification
through regional applications to pollen data. Where possible the scheme devised for Latin America conforms to existing classification and definitions. However, some of the specific vegetation types in Latin America were not adequately
covered by the existing range so two new PFTs (heath and
cushion plants) were added. To aid in the separation of the
African forest/savanna boundary, Jolly et al. (1998a) subdivided the tropical raingreen trees PFT (Tr) into three groups.
In the case of Latin America, it was decided that the overlap
(taxa being multiply assigned to the PFTs) between the PFTs
would be too great, and the distinction somewhat minimal.
Furthermore, the tropical xerophytic trees and shrubs PFT
encompass many taxa that would be assigned to the driest
tropical raingreen category. Therefore, the Tr PFT was subdivided into “wet” (Tr1 ) and “dry” (Tr2 ) tropical raingreen
trees.
The cornerstone of research concerned with the composition and distribution of Latin American vegetation, be it in
a contemporary time frame, or one that aims to work in the
past, is a good understanding of the ecology and distribution
of the taxa concerned. The Latin American pollen taxa were
assigned to one or more PFTs depending on the modern ecological range of the most important (i.e. most abundant) taxa
responsible for producing the pollen identifiable within the
modern data set. These assignments were made following
reference to the known biology of plants from several floras
(Rzedowski, 1983; Schofield, 1984; Wingenroth and Suarez,
1984; Kahn and de Granville, 1992; Gentry, 1993; Maberly,
1993; Seibert, 1996), botanical and palynological studies
(Beard, 1955; van der Hammen, 1963; 1972; Wijmstra and
van der Hammen, 1966; Eiten, 1972; Cleef and Hooghiemstra, 1984; Hooghiemstra and Cleef, 1984; Pires and Prance,
1985; Prance, 1985; Cuatrecasas and Barreto, 1988; Brown
and Lugo, 1990; Bush, 1991; Dov Par, 1992; Kappelle, 1993;
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741
Duivenvoorden and Cleef, 1994; Witte, 1994; Armesto et
al., 1995; Harley, 1995; Kappelle, 1995; Kershaw and McGlone, 1995; Veblen et al., 1995; Colinvaux, 1996; Grabherr,
1997; Hooghiemstra and van der Hammen, 1998) and personal communication with modern ecologists and palaeoecologists. Much of this information has been collated into
a dictionary on the distribution and ecology of parent taxa
of pollen lodged within the Latin American Pollen Database
(Marchant et al., 2002c). The resultant taxon vs. PFT assignments are presented in Table 4. Due to the high intra-generic
diversity, and also the wide range of ecology’s exhibited by
the parent taxa present within some genera, a number of taxa
were multiply assigned to a number of PFTs; where possible, pollen taxa were assigned to the PFTs within which the
parent taxa are most common.
Thus, the identified PFTs from Latin America are described by the suite of pollen taxa assigned to them, in turn
the biomes are distinguished by the suite of constituent PFTs
(Table 5). A number of pollen taxa belong to more than one
PFT (Table 4), and, as is the case with the potential vegetation, most PFTs contribute to more than one biome (Table 5).
Two problems can arise here for our analysis that can be circumvented by manipulation of the input matrices and output
biome affinity scores (the numerical sum computed for the
pollen spectra for each sample this is equal to the sum of
the square root of the percentages of the pollen types). First,
pollen samples can have equal maximum affinity with more
than one biome; this commonly occurs when the PFTs characteristic of one biome are a subset of another biome. Assigning the biomes so that subsets always come first in the
analysis solves the problem. A second problem arises where
multiple samples fall within the time window we are interested in. Multiple samples from a single site may have maximum affinity to a number of different biomes; the chance of
this is high when the score of the “best” biome is close to
that of the next “best” and the dominant biome switches between the top scoring biomes. In such cases, the “majority”
biome is mapped. For example, site A contains eight samples
within the time frame of 6000±500 14 C yr BP, five samples
have the greatest affinity to biome 1, two samples to biome
2 and one sample to biome 3. The result is that biome 1 is
mapped for site A at 6000±500 14 C yr BP. This situation is
not ideal as it masks the transient nature of the ecosystem
to environmental shifts, however, this is a problem with the
biomisation technique and can be overcome at a single site or
few sites by mapping the biome scores rather than the most
dominant biome (Marchant et al 2002a, b).
To enable a definition of the biomes to be based on bioclimatic data the climate space encompassed by Latin America (as abstracted from the climate data set of Leemans
and Cramer, 1991) was used to plot plant available moisture (α) against a temperature gradient (MTCO). This plot
was used to map the locational range of the biomes and to
identify the location of the sites used in the analysis where
there are pollen data for each of the three time periods in
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742
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
two-dimensional bioclimatic space (Fig. 5). Two immediate issues are apparent from this plot – firstly there is a bias
of sites with an α of 1 within Latin America and a potentially different measure of moisture could provide some definition in this area of the plot. Secondly, as would be expected, there is little desert bioclimatic space in Latin America and no pollen sites located in this area of the plot. The
twelve biomes identified within Latin America (Table 2) are
designed to incorporate the range of major vegetation types
and ensure consistency with previous areas to undergo the
process within the BIOME-6000 community. Biomes were
reconstructed from pollen data at sites with surface sample,
trap and radiocarbon-dated core-top data. The results were
used to produce a modern pollen-derived biome dot map
(Fig. 7); for each site a colour dot records the reconstructed
biome with the highest affinity score. These were compared
site by site, with the potential modern vegetation distribution
(Fig. 2). The biomisation procedure was applied to the fossil
datasets without modification. Results for all sites and periods are provided in Table 6 which allows a site-by-site comparison through time and a comparison between the modern
reconstruction and potential vegetation.
3
3.1
Results
Modern pollen vs. potential biome reconstruction
Visual comparison shows that the biomes reconstructed from
modern pollen data (Fig. 7) reflect the broad features in the
potential vegetation map (Fig. 2). In particular the modern reconstruction reproduces the transition from relatively
mesic vegetation types, around the coastal areas of South
America, to the more xeric biomes towards the interior.
For example, in eastern Argentina there is a transition from
steppe to xerophytic woods and scrub. Warm temperate rain
forest is an important biome in the southern and southeastern Brazilian highlands, with tropical dry forest being reconstructed towards the interior. Notable from this region
is the large number of different biomes being reconstructed
in a relatively small area. In part this reflects the variability of potential vegetation, not portrayed in our relatively
coarse resolution vegetation map (Fig. 2). For example, a
site recording tropical rain forest reflects the sites’ lowland
position where it is characterised by moist gallery forest with
a number of typical rain forest taxa present. Steppe is correctly reconstructed from the grasslands of south-eastern Argentina and dry forest in central Argentina, mirroring the
transition to “drought-deciduous thorn forests” of central Argentina (Schmithüsen, 1976). Steppe is assigned farther west
at approximately 1000 m in the Andes, southernmost South
America and northeast Brazil. The vegetation of southern
South America is dominated by cool temperate rain forest. The failure of the analysis to pick up the transition
from cool temperate rain forest to cool grass/shrubland as
Clim. Past, 5, 725–767, 2009
one progresses east along Tierra del Fuego stems from the
pollen spectra having a relatively large amount of Nothofagus
pollen. Moving northwards from southern South America
there is a transition to cool mixed forest, cool grass/shrubland
and steppe; these latter assignments are particularly associated with eastern flanks of the Cordillera de los Andes.
The concentration of sites along the Andes results in a
wide range of reconstructed biomes being geographically adjacent to each other when mapped in two-dimensional space
(Fig. 7). This phenomenon is most apparent in the northern Andes where the altitudinal, and therefore climatic gradients are at their steepest. Despite these rapid environmental
changes, the biome assignments reflect the changing vegetation patterns (Table 6). There is an altitudinal transition with
low altitudes (<300 m) being mainly assigned to the tropical rain forest, tropical dry forest, tropical seasonal forest
and steppe. Sites located at mid altitudes are described by
a number of different biomes including tropical seasonal forest, warm mixed forest, cool mixed forest and cool temperate
rain forest. Within this wide range of warm temperate rain
forest and tropical seasonal forest are commonly assigned
at lower elevations (Fig. 7, Table 6). Many of the sites at
high altitude have a high affinity to the cool grass/shrubland
biome. The line of the Andes can be easily seen by the cool
grass/shrubland biome assignments, these being commonly
recorded at sites above 3800 m. The warm temperate rain forest is assigned at lower elevations and is analogous to Andean
forest, being dominated by Podocarpus, Quercus and Weinmannia comprises a different forest type to that assigned in
southern and southeast Brazil or southern South America. A
mixture of biomes presently characterises the Amazon Basin
with only four sites recording the tropical rain forest biome;
two of these are in coastal locations. Tropical seasonal forest
is recorded in four locations; this representing a slightly drier
type of forest than tropical rain forest, containing some deciduous taxa. A number of “Amazonian” sites record warm
temperate rain forest, these assignments responding to the
presence of Andean floristic elements within lowland vegetation. There are a number of sites that record tropical dry
forest, this being relatively widespread, e.g. on Easter Island,
lowland Colombia and the Brazilian interior. Warm temperate rain forest describes the majority of the sites in the Panamanian and southern Costa Rican isthmus with warm mixed
forest, being commonly at higher altitudes. This is an area
where the comparison between the observed and predicted
biomes shows a discrepancy; the possible reasons behind this
will be discussed fully. Warm mixed forest is correctly assigned to the highlands of central and southern México as is
tropical dry forest in southern México.
Investigating the correspondence between the pollenbased reconstruction and the potential vegetation for individual biomes provides a check on the methodology,
particularly the construction of the matrices. The cool
grass/shrubland biome is accurately reconstructed at the majority of sites. The sites that do not match the potential
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R. Marchant et al.: Pollen-based biome reconstructions for Latin America
743
Table 4. Latin American pollen taxa used in the biomisation analysis and their assignment to the plant functional types (PFTs).
PFT Codes
Pollen Taxa
g
Poaceae.
man
Acrostichum-type, Avicennia, Laguncularia, Rhizophora.
tx
Alsophila, Alstroemeria, Cnemidaria, Cyathea, Dicksonia, Nephelea.
Tr1
Alibertia, Anacardiaceae, Andira-type, Astronium, Bauhinia, Bombacaceae, Bougainvillea, Caesalpineae, Caseariatype, Clematis, Coccoloba, Copaifera, Didymopanax, Eugenia, Euterpe, Goupia-type, Guapira, Heliocarpus, Hura,
Hieronima, Hippocrateaceae, Ipomoea, Laplacea, Lecythidaceae, Leguminoseae, Loranthaceae, Macharium, Macrolobium, Malpighiaceae, Malvaceae, Maytenus, Ocotea-type, Pera, Phyllostylon, Piper, Pisonia, Psychotria, Rubiaceae,
Rutaceae, Salix, Sapium, Simira, Siparuna, Spirotheca, Spondias, Symplocos, Tecoma, Trema, Xylosma, Zanthoxylum.
Tr2
Acacia, Alchornea, Alibertia, Andira-type, Anacardiaceae, Apocynaceae, Astronium, Banisteriopsis, Bauhinia, Bombacaceae, Bougainvillea, Brosimum, Brunellia, Bulnesia, Bumelia-type, Bursera, Byrsonima, Caesalpineae, Celastraceae, Chrysophyllum, Clematis, Combretaceae, Copaifera, Cordia, Coriaria, Cuphea, Curatella, Didymopanax,
Elaeocarpaceae, Eryngium, Erythrina, Euterpe, Gallesia, Guapira, Hieronima, Hura, Hymenophylleace, Ipomoea, Loranthaceae, Malvaceae, Maytenus, Melastomataceae, Meliaceae, Mimosa, Passiflora, Pera, Phyllostylon, Piper, Pisonia, Protium, Pseudobombax, Rhamnaceae, Rutaceae, Salix, Sapium, Schinus, Simira, Siparuna, Spirotheca, Spondias,
Styrax, Symplocos, Tiliaceae, Trema, Xylosma, Zanthoxylum.
Te1
Abutilon, Actinostemon concolor, Alchornea, Amanoa, Apeiba, Apocynaceae, Araliaceae, Arecaceae, Arrabidaea, Aspidosperma, Astrocarium, Begoniaceae, Bignoniaceae, Bombacaceae, Bonamia, Brosimum, Brunellia, Brownea, Calliandra, Campomanesia, Cardiospermum, Castilla, Cecropia, Cedrela, Celtis, Copaifera, Coprosma, Cucurbitaceae,
Cunoniaceae, Dalbergia, Dioclea, Doliocarpus, Elaeagia, Euterpe, Ficus, Flacourtiaceae, Forsterania, Geonoma,
Guapira, Guazuma, Guarea, Hura, Ilex, Inga, Iriartea, Iva xanthifolia-type, Lecythidaceae, Leguminoseae, Licania, Mabea, Macharium, Macrolobium, Macrocarpea, Malpighiaceae, Mauritia, Marcgraviaceae, Maripa, Marattia, Matayba, Melastomatacae, Meliaceae, Miconia, Moraceae, Myrsine, Myrtaceae, Mauritiella, Nyctaginaceae,
Ochnaceae, Ocotea-type, Oenocarpus, Oreopanax, Palmae, Panopsis, Parahancornia, Passiflora, Plenckia, Protium,
Pseudopanax laetevirens, Rauvolfia, Rhipsalis, Rubiaceae, Sapotaceae, Scheelea, Scleronema, Socratea, Sloanea,
Solanaceae, Sophora, Strutanthus, Symphonia, Swartzia, Taperira, Ternstroemia cf. T . brasiliensis, Tetrochiduim,
Tetraploa aristata, Thymelaeaceae, Tiliaceae, Trichilia, Trigonia, Vismia, Warsewiczia.
Te2
Abutilon, Acacia, Aegiphila, Alibertia, Apeiba, Apocynaceae, Aspidosperma, Boraginaceae, Bougainvillea, Brosimum,
Brunellia, Calliandra, Cecropia, Cedrela, Celtis, Combretaceae, Croton, Cucurbitaceae, Dalbergia, Didymopanax, Dioclea, Forsterania, Hippocrateaceae, Humiria, Humulus, Ilex, Iriartea, Leguminoseae, Macrolobium, Mauritia, Melastomataceae, Miconia, Moraceae, Mysine, Myrtaceae, Ochnaceae, Ocotea-type, Palmae, Panopsis, Passiflora, Plenckia,
Pseudopanax, Psychotria, Sapotaceae, Scleronema, Serjania, Sophora, Strutanthus, Swartzia, Taperira, Tiliaceae, Vismia, Warsewiczia.
wtc
Abies, Araucaria, Juniperus, Pinus.
ctc2
Abies, Araucaria, Araucaria augustifolia, Cupressaceae, Dacrydium, Juniperus, Pilgerodendron, Podocarpus, Prumnopitys andina, Saxegothaea conspicua.
ctc1
Austrocedrus chilensis, Cupressaceae, Dacrydium, Fitzroya cupressoides-type, Pilgerodendron, Pinus, Podocarpus,
Prumnopitys andina, Saxegothaea conspicua, Taxodium.
txts
Acacia, Aeschynomene, Agave, Anthurium, Aphelandra, Arrabidaea, Atamisquea, Ayenia, Bursera, Byrsonima, Byttneria, Cabomba, Cactaceae, Caryocar, Cayaponia, Cercidium, Chomelia, Chrysophyllum, Chuquiragua, Cissus, Clusia, Combretaceae, Convolvulaceae, Cordia, Cuphea, Curatella, Dodonaea, Echinodorus, Eichhornia, Evolvulus, Hippocrateaceae, Humulus, Hyptis, Ipomoea, Larrea, Lithraea, Malpighiaceae, Manihot, Maprounea, Menispermaceae,
Metopium, Miconia, Mimosa cf M.taimbensis, Palicourea, Peperommia, Phaseolus, Phyllanthus, Polygala, PolylepisAcaena, Pouteria, Portulaccaceae undiff., Prosopis, Rhamnaceae, Sapium, Schefflera, Schinus, Sebastiana, Serjania,
Solanaceae, Sloanea, Stryphnodendron, Tecoma, Trixis, Zornia.
h
Arenaria, Aristotelia, Asteraceae, Berberidaceae, Berberis, Empetrum, Ericaceae, Sisyrinchium-type.
ds
Agave, Atamisquea, Cactaceae, Ephedra, Monttea aphylla.
df
Alternanthera, Ephedra, Monttea aphylla, Xyris.
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R. Marchant et al.: Pollen-based biome reconstructions for Latin America
Table 4. Continued.
PFT Codes
Pollen Taxa
tf
Acalypha, Acanthaceae, Alchemilla, Alismataceae, Alsophila, Anemia, Antheroceros, Armeria, Artemisia, Assulina,
Asteraceae, Astelia, Begonia, Bernardia, Brassicaceae, Bravaisia, Bromeliaceae, Calyceraceae, Caperonia, Caryophyllaceae, Cassia, Cichoriaceae, Cirsium, Cruciferae, Eriogonum, Eriocaulaceae, Euphorbia, Euphorbiaceae, Fabaceae,
Geraniaceae, Gomphorena, Gunnera, Hebenaria, Iresine, Justicia, Lamiaceae, Laportea, Liquidambar, Liliaceae, Lobelia, Menispermaceae, Muehlenbeckia, Nertea, Onagraceae, Orchidaceae, Pilea, Polygala, Rhaphithamnus, Rhus, Rubiaceae, Smilax, Triumfetta, Umbelliferae, Urticaceae, Verbena, Verbenaceae, Vernonia, Viburnum, Vitis.
tef
Acalypha, Acanthaceae, Apiaceae, Apium, Artemisia, Astelia, Azara, Borreria, Brassicaceae, Bravaisia, Bromeliaceae,
Cassia, Cichoriaceae, Eriocaulaceae, Eriogonum, Euphorbia, Euphorbiaceae, Fabaceae, Genipa, Gordonia, Gunnera,
Hippeastrum, Hydrocotyle, Iridaceae, Jungia, Justicia, Lachemella, Lamanonia, Lamiaceae, Laportea, Liliaceae, Lupinus, Malvaceae, Marcgraviaceae, Moritzia-type, Mutisia, Nertea, Onagraceae, Orchidaceae, Pamphalea, Perezia, Phaseolus, Pilea, Piscidia, Plantago, Polemoniaceae, Polygala, Portulaccaceae undiff., Pouteria, Ranunculaceae, Rubiaceae,
Rumex, Satureja, Scrophulariaceae, Selaginella, Thalictrum, Triumfetta, Umbelliferae, Urticaceae, Verbenaceae, Vernonia, Vicia, Vitis, Wendtia, Xyris.
sf
Alternanthera, Amaranthaceae/Chenopodiaceae, Antheroceros, Armeria, Assulina, Asteraceae, Astelia, Borreria, Calyceraceae, Cardus, Caryophyllaceae, Cardus, Connarus, Cruciferae, Embothrium, Eriogonum, Eryngium, Euphorbia, Euphorbiaceae, Fabaceae, Geraniaceae Gomphorena, Gunnera, Hebenaria, Hippeastrum, Hydrocotyle, Iridaceae, Jungia, Justicia, Liliaceae, Lamiaceae, Liquidambar, Mutisia, Nanodea, Nassauvia-type, Orchidaceae, Oxalis,
Phacelia, Physalis, Plantago, Polemoniaceae, Pouteria, Ranunculaceae, Restionaceae, Rosaceae, Rubiaceae, Satureja,
Scutellaria-type, Umbelliferae, Urticaceae, Vicia, Vitis, Wendtia, Xyris.
af
Arenaria, Astragalus, Azorella, Bartsia-type, Borreria, Bromeliaceae, Bravaisia, Campanulaceae, Cardus, Caryophyllaceae, Deschampsia antarctica, Diphasiastrum complanatum-type, Donatia, Draba, Epilobium, Eriocaulaceae Eriocaulon, Eriogonum, Gaimardia, Gilia, Halenia, Hebenaria, Hippeastrum, Hydrocotyle, Iridaceae, Jamesonia, Labiatae,
Lachemella, Lamiaceae, Liquidambar, Lupinus, Lysipomia, Montia, Moritzia-type, Muehlenbeckia, Nassauvia-type,
Orchidaceae, Oxalis, Perezia, Plantago, Puya, Quinchamalium, Relbunium, Rosaceae, Rubiaceae, Rumex, Satureja,
Scrophulariaceae, Scutellaria-type, Selaginella, Sisyrinchium-type, Umbelliferae, Valeriana, Viola.
cp
Apiaceae, Azorella, Gaimardia, Montia, Plantago, Saxifraga.
wte
Aegiphila, Allophylus, Aphelandra, Araliaceae, Azara, Baccharis, Bauhinia, Begoniaceae, Buddleja, Bumelia-type, Clusia, Croton, Daphnopsis, Desfontainia, Elaeocarpaceae, Embothrium, Eucryphia/Caldcluvia paniculata, Euterpe, Fuchsia, Geonoma, Geraniaceae, Griselinia, Guettardia, Gunnera, Guttiferae, Hedyosmum, Heliocarpus, Humiria, Labiatae,
Lomatia/Gevuina, Loranthaceae, Ludwigia, Luehea, Malpighiaceae, Matayba, Melastomatacae, Meliaceae, Mimosa,
Mimosa cf. M. scabrella, Mutisia, Myrica, Mysine, Nothofagus obliqua-type, Oreopanax, Palicourea, Proteaceae,
Prunus, Pseudopanax laetevirens, Psychotria, Quercus, Roupala, Sambucus, Solanaceae, Stryphnodendron, Styloceras,
Tepualia stipularis, Tetrochiduim, Thymelaeaceae, Trichilia, Verbenaceae, Viburnum, Warsewiczia, Weinmannia.
wte1
Aegiphila, Alnus, Arecaceae, Aragoa, Arcytophyllum, Aristotelia, Azara, Banara, Banisteriopsis, Begoniaceae, Bocconia, Brunellia, Buddleja, Calandrinia, Campanulaceae, Celastraceae, Chuquiraga, Clethra, Daphnopsis, Desfontainia,
Dodonaea, Drimys, Epacridaceae, Ericaceae, Fuchsia, Galium, Gaultheria ulei, Geraniaceae, Gesneriaceae, Hedyosmum, Hydrangea, Hypericum, Labiatae, Loranthaceae, Ludwigia, Maytenus, Meliaceae, Meliosma, Muehlenbeckia,
Myrtaceae, Mysine, Nothofagus, Nothofagus antarctica-type, Ostrya-type, Proteaceae, Prunus, Pseudopanax laetevirens, Quercus, Ribes, Roupala, Sambucus, Solanaceae, Tepualia stipularis, Verbenaceae, Viburnum, Weinmannia.
wte4
Abatia, Adesmia, Alchemilla, Alfaroa, Arcytophyllum, Assulina, Asteraceae, Clethra, Colignonia, Dodonaea, Ericaceae,
Gaiadendron, Gaultheria ulei, Guttifera, Laurelia, Muehlenbeckia, Myrteola, Polylepis-Acaena, Ribes, Rosaceae,
Tetrochiduim, Weinmannia.
ts
Alnus, Banksia, Carpinus, Cayaponia, Fagus, Fraxinus, Juglans, Loranthaceae, Luehea, Myrica, Populus, Styrax,
Trema, Ulmaceae, Vallea.
ts1
Escallonia, Eugenia, Gordonia, Liquidambar, Luehea, Misodendrum, Myzodendron, Styrax, Ternstroemia cf. T .
brasiliensis, Trema, Vallea.
aa
Aragoa, Arcytophyllum, Arenaria, Asteraceae, Baccharis, Cruciferae, Draba, Empetrum, Ephedra, Ericaceae, Eriogonum, Escallonia, Gentiana, Gentianaceae, Hypericum, Nassauvia-type, Puya, Rosaceae, Senecio.
Clim. Past, 5, 725–767, 2009
www.clim-past.net/5/725/2009/
Figure 5. Theoretical biome scheme for Latin America portrayed on a grid of environmental space along the gradients of
temperature (mean
temperature
of the coldest month)for
and plant
available
moisture (alpha). Grey fields reflect the climate
R. Marchant et al.: Pollen-based
biome
reconstructions
Latin
America
745
space in 100 x 100 km grid cells from Mexico to Patagonia as derived from Leemans and Crammer (1991). The location of
sites where pollen data is used for the translation to biomes across Latin America are shown as red triangles.
clim
Moisture (Alpha)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
pollen
0.9
1
-10
CGSH
-5
0
Temperature (MTCO)
COMI
5
10
15
D
E
S
E
S
T
E
P
X
E
R
O
T
D
F
O
CTRF
WEFO
WAMF
WTRF
20
25
TSFO
TRFO
30
Fig. 5. Theoretical biome scheme for Latin America portrayed on a grid of environmental space along the gradients of temperature (mean
temperature of the coldest month) and plant available moisture (alpha). Grey fields reflect the climate space in 100×100 km grid cells from
Mexico to Patagonia as derived from Leemans and Crammer (1991). The location of sites where pollen data is used for the reconstruction of
biomes across Latin America are shown as red triangles.
Table 5. Assignment of Latin American plant functional types to
Biomes.
CODES
Plant functional types
TRFO
TSFO
TDFO
WTRF
WEFO
CTRF
WAMF
COMI
STEP
DESE
CGSH
CGSS
man, tx, Te1 , Te2 , tf
tx, Tr1 , Tr2 , Te2 , tf
Tr2 , tf, txts, df
tx, Tr1 , Te1 , wtc, ctc2 , tef, wte
tx, Tr2 , wtc, ctc2 , ec, tef, wte
tx, h, ctc1 , tef, wte, wte1
Tr2 , wtc, tef, wte, ts
ctc1 , tef, wte1 , wte4 , ts1
sf
ds, df
af, aa, wte4 , h
af, aa, cp
vegetation commonly result from the inclusion of high altitude arboreal pollen, this resulting in assignments of cool
mixed forest and cool temperate rain forest. The other common assignment is towards steppe; the dominance of the
pollen spectra by Poaceae, and lack of shrubby taxa, result
in the assignment to the steppe biome. Indeed, the affinity
scores to the cool grass/shrubland and steppe biome at most
sites, where one of these biomes is dominant, is normally
quite close. For the cool mixed forest biome 66% of sites
accurately reconstruct the potential vegetation. The 34% of
“wrong” assignments mainly result in either a reconstruction
of cool grass/shrubland, thought to represent possible forest
clearance, and the dominance of the vegetation by grassland,
www.clim-past.net/5/725/2009/
or cool temperate rain forest biome due to the numerous
shared taxa between these two biomes. 75% of cool temperate rain forest biome reconstructions match the potential
vegetation at the site. The remaining 25% of the sites mainly
show either warm mixed forest or warm temperate rain forest
assignments. For the tropical dry forest biome some 90% of
the sites accurately reflect the potential vegetation at the site.
For the remaining 10% of “wrong” assignments the common
result is towards a cool temperate rain forest or steppe. For
the tropical rain forest biome 85% of the sites accurately reflect the potential vegetation. For the sites that do not match,
a common reconstruction is warm temperate rain forest. This
can be explained by the number of Andean elements being
present within lowland tropical forests with a couple of sites
reconstructing the closely related biome of tropical seasonal
forest. This facet of the pollen data is also exemplified by a
number (35%) of the tropical seasonal forest sites recording
the warm temperate rain forest biome. Warm evergreen forest is correctly assigned at 80% of the sites. Warm temperate
rain forest is assigned correctly at 78% of sites. 75% of the
sites that do not reconstruct this biome “correctly” lead to
assignments of tropical rainforest and tropical seasonal forest at low altitudes (<500 m) and cool mixed forest at high
(>2000 m).
The generally correct biome assignments, in relation to a
map of potential vegetation confirm the robustness of our application of the biomisation method to Latin America. Where
the match between pollen and potential vegetation reconstructions is relatively low (tropical seasonal forest and warm
temperate rain forest), then a common forcing factor, that
of “high altitude” plants presently growing at low altitudes
Clim. Past, 5, 725–767, 2009
746
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
Table 6. Site locations showing biome changes from the present, 6000±500 and 18 000±1000 14 C yr BP. The relationship between the
present observed biome and the biome reconstructed from the modern pollen data is also shown.
Site
Lake Åsa3
Harberton
Puerto del Hambre
La Misión
Punta Arenas
Meseta Latorre 1
Meseta Latorre 2
Torres del Paine
Moreno Glacier Bog
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Patagonia
Dichan
Puerto Eden
La Esperanza
LagoBsAs
Laguna Six Minutes
Laguna Stibnite
Laguna Stibnite
Pico Salam
Laguna Lincoln
Laguna Lofel
Estero Huitanque
AustroEsqu
Mayol
Puchilco
AlercesNor
San Pedro
Chepu
Clim. Past, 5, 725–767, 2009
Long.
Latitude
Alt.
Present biome
Modern
−61.13
−67.16
−70.92
−67.83
−70.97
−72.05
−72.03
−72.66
−73.00
−72.90
−72.90
−72.70
−72.00
−71.70
−71.50
−71.10
−69.90
−69.30
−71.30
−70.90
−70.20
−69.60
−72.98
−70.50
−72.95
−72.90
−72.85
−72.75
−72.80
−72.55
−71.95
−69.00
−68.90
−72.00
−72.90
−68.60
−68.10
−68.30
−73.88
−74.41
−72.83
−71.45
−74.33
−74.43
−74.38
−67.43
−74.07
−74.43
−73.82
−71.47
−73.75
−73.62
−71.60
−73.95
−73.66
−62.62
−54.88
−53.59
−53.5
−53.15
−51.52
−51.44
−50.98
−50.46
−50.25
−50.20
−50.20
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.15
−50.10
−50.10
−50.10
−50.05
−50.05
−50.05
−50.05
−50.00
−50.00
−49.66
−49.13
−46.63
−46.44
−46.43
−46.43
−46.43
−45.42
−45.34
−44.85
−43.61
−42.66
−42.64
−42.63
−42.56
−42.25
−42.17
35
20
5
5
75
980
1000
100
200
50
20
80
150
180
180
180
180
180
190
190
190
190
20
200
50
50
60
60
70
100
180
190
100
150
50
50
0
20
50
50
330
230
50
50
50
637
50
50
52
1100
75
110
800
650
140
CGSH
STEP
CTRF
STEP
CTRF
CGSH
CGSH
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
STEP
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CGSH
STEP
CTRF
STEP
CTRF
COMI
COMI
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
WAMF
CTRF
CTRF
CTRF
STEP
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CGSH
CTRF
CTRF
6000
STEP
CTRF
STEP
CTRF
CTRF
CGSH
TDFO
COMI
18 000
CGSH
CTRF
CTRF
CTRF
CGSH
CTRF
CTRF
CTRF
COMI
CTRF
CGSH
CGSH
CGSH
CTRF
CTRF
www.clim-past.net/5/725/2009/
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
747
Table 6. Continued.
Site
Mallin Book
Comallo
Puerto Octay PM13
Espuma
Primavera
Encantado
Ruta 3.4
Ruta 3.3
Caunahue
Ruta 250.19
Pedro Luro
Origone
Gaviotas
Salina Anzotegui
Cueva Haichol
Arroyo Sauce Chico
Cerro La China
Empalme Querandı́es
Veranada Pelan
Vaca Lauquen
Veranada Vulkanpickel
Salado
Salina 2
Serra do Rio Rastro
Morro da Igreja
Serra da Boa Vista
Rano Kao
Rano Raraku Bore 3
Rano Aroui
Poço Grande
Reserva Volta Velha
Atlantic
Colombo
Serra Campos Gerais
Aguilar
Rio da Curuá
Tumbre 2
Aguas Calientas
Botucatu
Curcuab
Lagoa da Caço
Morro de Itapeva
Assis
Lagoa Santa
Bauru
Brotas
Salitre
Ajata
Sajama
Lago do Pires
Wasa Mayu
Crominia
Mt. Blanco
Lake Huinãmimarca
Cerro Calvario
www.clim-past.net/5/725/2009/
Long.
Latitude
Alt.
Present biome
Modern
6000
−71.58
−70.21
−72.90
−63.25
−71.18
−71.13
−62.79
−62.59
−72.00
−65.58
−62.53
−62.43
−63.65
−63.77
−70.66
−62.23
−58.64
−60.65
−70.38
−71.08
−70.41
−69.75
−69.33
−49.55
−49.86
−49.15
−109.43
−109.28
−109.40
−48.86
−48.38
−48.35
−49.23
−50.21
−65.75
−48.83
−67.78
−67.42
−48.00
−48.00
−43.43
−45.63
−50.50
−47.45
−49.07
−48.08
−46.78
−69.20
−68.88
−42.21
−65.91
−49.45
−67.35
−69.00
−68.50
−41.33
−41.01
−40.93
−40.67
−40.66
−40.66
−40.50
−40.08
−40.00
−39.54
−39.50
−39.08
−39.07
−39.06
−38.58
−38.07
−37.84
−37.00
−36.88
−36.83
−36.68
−35.33
−32.25
−28.55
−28.18
−27.70
−27.18
−27.16
−27.08
−26.41
−26.04
−25.95
−25.33
−24.66
−23.83
−23.83
−23.31
−23.08
−23.00
−23.00
−22.97
−22.78
−22.68
−22.36
−22.32
−22.29
−19.00
−18.25
−18.16
−17.95
−17.54
−17.28
−17.02
−16.50
−16.50
800
815
120
50
800
960
20
20
500
117
20
20
90
−5
1050
85
200
105
1860
1450
2800
3200
2000
1420
1800
1160
110
75
425
10
0
200
920
1200
4000
800
3880
4210
700
700
5
1850
540
630
570
700
1050
4700
4250
390
2720
200
4780
3765
3950
CTRF
CGSH
CTRF
TDFO
CTRF
CTRF
STEP
STEP
CTRF
STEP
STEP
STEP
TDFO
STEP
STEP
STEP
STEP
STEP
CGSH
CTRF
CGSH
CGSH
CGSH
WTRF
WTRF
WTRF
TDFO
TDFO
TDFO
WTRF
WTRF
WTRF
TSFO
WTRF
CGSH
WTRF
CGSH
CGSH
WTRF
WTRF
TDFO
WTRF
TSFO
TDFO
TSFO
WTRF
WTRF
CGSH
CGSH
TSFO
COMI
TDFO
CGSS
CGSH
CGSH
COMI
STEP
CTRF
TDFO
CGSH
COMI
STEP
STEP
CTRF
CTRF
18 000
COMI
TDFO
CTRF
STEP
STEP
STEP
TDFO
STEP
STEP
STEP
TDFO
STEP
CGSH
COMI
STEP
CGSH
STEP
WTRF
WTRF
WTRF
TDFO
TDFO
TDFO
WTRF
WTRF
WTRF
TSFO
WAMF
CTRF
CGSH
CGSH
WTRF
WTRF
WEFO
TSFO
TDFO
TSFO
WTRF
WTRF
CGSH
CGSH
TSFO
TDFO
CGSH
CGSH
STEP
STEP
TDFO
TDFO
TDFO
CGSH
TDFO
STEP
CGSH
WTRF
CTRF
TDFO
CGSH
TDFO
WTRF
TDFO
STEP
TRFO
STEP
CGSH
TDFO
TDFO
STEP
CGSH
TDFO
CGSH
TSFO
CGSH
TSFO
CGSS
CGSH
CGSH
STEP
TDFO
CGSS
Clim. Past, 5, 725–767, 2009
748
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
Table 6. Continued.
Site
Rio Kaluyo
Chacaltaya 1
Cumre Unduavi
Rio Jequitinhonha
Brasilia 1
Aguads Emendadas
Cuiaba
Amarete
Cotapampa
Katantica
Laguna Chaplin
Rio de Contas
Laguna Bella Vista
Laguna Jeronimo
Laguna Pomacocha
Laguna Paca
Laguna Tuctua
Laguna Milloc
Laguna Junin2
Laguna Huatacocha
Rio São Francisco
Saquinho
Rio São Francisco
Katira
Campina Grande I
Lagoa Grande
Picos
Lago Bolim
Rio Protengi
Mirim
Rio Mirim
Rio (unclear)
Carajas
Ciudad Universitaria X
Rio Jaguaribe II
Rio Jaguaribe I
Lake Surucucho
Ayauch
Llaviucu
Indanza
Kumpack
Puyo Bog
Rum Tum
Yambo
Lago Crispim
Lagoa da Curuça2
Mariñame-II
Monica-1
Añangucocha
Limoncocha
Cayambe
Lago Agrio
San Marcos
Santa Cecilia
Lake Santa Cecilia
Cuyabeno
Clim. Past, 5, 725–767, 2009
Long.
Latitude
Alt.
Present biome
Modern
6000
−68.13
−68.13
−68.03
−38.92
−47.66
−47.58
−55.86
−68.98
−69.11
−69.18
−61.05
−39.00
−61.56
−75.21
−75.50
−75.50
−75.00
−76.35
−76.18
−76.55
−43.00
−43.23
−36.50
−63.00
−35.75
−47.45
−41.40
−35.18
−35.25
−35.30
−35.40
−38.00
−48.00
−74.18
−37.76
−37.75
−78.95
−78.13
−79.43
−78.83
−78.51
−79.06
−79.03
−79.03
−48.00
−47.85
−72.03
−72.50
−77.03
−76.66
−78.03
−77.03
−79.03
−77.03
−77.02
−77.01
−16.43
−16.36
−16.33
−15.85
−15.59
−15.56
−15.35
−15.23
−15.21
−14.8
−14.50
−14.28
−13.58
−11.78
−11.75
−11.71
−11.66
−11.56
−11.00
−10.76
−10.46
−10.44
−10.26
−9.00
−7.23
−7.08
−7.06
−6.04
−5.78
−5.68
−5.64
−5.50
−5.00
−4.75
−4.55
−4.43
−3.75
−2.09
−1.83
−1.53
−1.53
−1.43
−1.13
−1.03
−0.8
−0.76
−0.66
−0.60
−0.53
−0.38
−0.03
0.03
0.03
0.04
0.06
0.08
4070
4750
4620
80
1030
200
350
4000
4450
4820
750
80
750
4450
4450
3600
4250
4325
4100
4500
400
480
80
750
70
75
70
90
80
70
70
50
150
2560
50
50
970
550
3120
2100
700
953
2392
2600
0
35
160
160
280
230
4350
330
3400
330
330
280
CGSH
CGSH
CGSS
TDFO
TDFO
TDFO
TDFO
CGSH
CGSH
CGSH
TSFO
TDFO
TSFO
CGSH
CGSH
CGSH
CGSH
CGSH
CGSH
CGSH
TDFO
TSFO
TDFO
TDFO
TSFO
TDFO
TDFO
TDFO
TDFO
TDFO
TDFO
TDFO
TSFO
COMI
TDFO
TDFO
WTRF
WTRF
CTRF
CTRF
WTRF
WTRF
CTRF
CTRF
TRFO
TRFO
TRFO
TRFO
TRFO
TRFO
CGSH
TRFO
CGSH
TRFO
TRFO
TRFO
CGSH
CGSH
CGSS
TDFO
CTRF
CTRF
TDFO
CGSH
CGSH
CGSH
TSFO
STEP
TSFO
CGSH
CGSH
CTRF
COMI
CGSH
COMI
CGSH
TDFO
TSFO
STEP
TDFO
STEP
CTRF
WTRF
STEP
CTRF
STEP
WTRF
CTRF
TSFO
COMI
TDFO
STEP
CTRF
WTRF
WTRF
WTRF
WTRF
WTRF
WTRF
WTRF
STEP
TRFO
TRFO
TRFO
WTRF
CTRF
CGSH
WTRF
TDFO
WTRF
TRFO
WTRF
STEP
CGSH
CGSS
18 000
TSFO
CGSH
CGSH
CGSH
CGSH
TDFO
TSFO
TSFO
CGSH
CGSH
WTRF
CGSH
COMI
CGSH
TSFO
WEFO
TDFO
CGSH
TDFO
TDFO
TSFO
WAMF
TSFO
WAMF
WTRF
TSFO
TSFO
TRFO
WTRF
TRFO
CGSH
www.clim-past.net/5/725/2009/
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
749
Table 6. Continued.
Site
Cunro
Mera
Lake Agrio
Yaguara cocha
La Pata
Lagoa das Patas
Piusbi
Pitalito
Piagua
Pantano de Genagra
Rio Timbio
El Camito
Loma Linda
Lago Agua Sucia
El Gobernador
La Guitarra
La Primavera
Corazón Partido
El Trinagulo
Carimagua
La Rabona
El Piñal
Alsacia
Andabobos
Ubaqué
De la América
Turbera de Calostros
Laguna Angel
Libano
de Pedro Palo III
Paramo Palacio
Greja
Sardinas
Herrera
Agua Blanca
El Abra II
Paramo de Peña Negra
ODP site 932
Comprida
Geral
Paramo de Laguna Verde
Fúquene II
Jotaordo
Cienaga del Visitador
Bobos
Ciega I
Valle de Lagunillas
Cana, Darien
Cana Swamp
Wodehouse Swamp
La Chonta
El Valle
La Yeguada,
Cerro Campana
Quebrada Nelson
Laguna Volcán
www.clim-past.net/5/725/2009/
Long.
Latitude
Alt.
Present biome
Modern
−79.03
−76.92
−76.92
−79.03
−66.66
−66.68
−77.89
−76.50
−76.50
−76.50
−76.50
−76.60
−73.35
−73.54
−75.00
−74.28
−74.13
−74.25
−74.25
−74.14
−74.25
−70.40
−74.11
−74.15
−73.55
−74.00
−73.48
−70.54
−75.50
−74.41
−73.88
−73.70
−69.45
−73.91
−74.45
−73.96
−74.09
−47.03
−47.63
−47.53
−74.00
−73.87
−76.66
−72.83
−72.85
−72.31
−72.34
−77.58
−77.59
−77.58
−82.00
−79.78
−80.78
−79.93
−82.31
−82.75
0.08
0.11
0.11
0.13
0.25
0.26
1.66
1.75
2.30
2.50
2.50
2.53
3.22
3.46
3.95
4.00
4.00
4.00
4.00
4.04
4.05
4.09
4.09
4.09
4.33
4.33
4.41
4.45
4.50
4.50
4.76
4.86
4.95
5.00
5.0
5.02
5.09
5.18
5.18
5.18
5.25
5.50
5.66
6.13
6.13
6.50
6.50
7.68
7.74
7.75
8.00
8.43
8.43
8.63
8.66
8.75
2800
1100
330
2210
300
300
200
1300
1700
1750
1750
50
310
260
3815
3450
3525
4100
4100
180
4000
185
3100
3570
2000
3550
3730
205
1820
2000
3550
4000
180
2000
3250
2570
3625
0
130
130
3625
2580
0
3300
3800
3510
3880
1000
500
500
2310
500
650
800
1130
1500
CTRF
WTRF
TRFO
CTRF
TRFO
TRFO
TRFO
WEFO
WEFO
WEFO
WEFO
TRFO
TDFO
TDFO
CTRF
COMI
CGSH
CGSH
CGSH
TDFO
CGSH
TDFO
COMI
CGSH
WEFO
CGSH
CGSH
TDFO
WEFO
COMI
CGSH
CGSH
TDFO
COMI
COMI
COMI
CGSH
TRFO
WTRF
WTRF
CGSH
COMI
TRFO
COMI
CGSH
COMI
CGSH
TSFO
TRFO
TRFO
CTRF
TSFO
TSFO
WTRF
WTRF
WTRF
WTRF
WTRF
TRFO
WTRF
TSFO
WTRF
TRFO
WEFO
WEFO
TDFO
WEFO
TRFO
TDFO
TDFO
CTRF
COMI
CGSH
CGSH
CGSH
TDFO
CGSH
TDFO
WAMF
CGSH
WEFO
CGSH
CGSH
TSFO
WEFO
WTRF
CGSH
CGSH
TDFO
CGSH
COMI
CTRF
CGSH
WTRF
WTRF
WTRF
CGSH
CGSH
TRFO
COMI
CGSH
CTRF
CGSH
TSFO
TRFO
TRFO
CTRF
TSFO
TSFO
WTRF
WTRF
WAMF
6000
18 000
WTRF
TSFO
TRFO
TSFO
WEFO
WEFO
WEFO
WTRF
WTRF
TDFO
STEP
WTRF
CTRF
COMI
CGSS
CTRF
TDFO
COMI
CGSH
TDFO
CTRF
TDFO
COMI
WTRF
CGSH
CGSH
TDFO
COMI
CTRF
CTRF
COMI
WTRF
TRFO
TRFO
CGSH
CTRF
CTRF
CGSH
TSFO
CTRF
CGSH
CTRF
COMI
WAMF
TDFO
TSFO
Clim. Past, 5, 725–767, 2009
750
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
Table 6. Continued.
Site
Valle Laguna Negra
Valle Laguna Victoria
Horsefly Ridge
Volcan Baru
El Bosque
Finca Lerida
Volcan Irazu
Paramo de Miranda
Panama
Ocelot Pond
Soberania
Paramo Piedras Blancas
Pipeline Rd
Barro Colorado Island
Lago Chirripó
Talamancas
Lago de las Morrenas
Bog 70
Tres de Junio
Bog 68
Asuncion
Carara Biological Reserve
Quebrador
Cataracta, Carara 1
Cataracta, Carara 2
Carara
Laguna Bonilla
Laguna Barva
Laguna Botos
Laguna Palmita
Bosque Alegre
Laguna González
Laguna Congo
Laguna Hule
Laguna Marı́a Aguilar
Monteverde, Heredia
Volcan Poas
Brauillo Carillo, Heredia
Lake Valencia
Laguna Rı́o Cuarto
La Selva, Heredia
La Pacifica, Guanacaste
Cantarrana Swamp
Sendro Sedro Swamp
Laguna La Palma
Laguna Cedeño
Tortuguero
Santa Rosa2
Santa Rosa1
Boca de Lopez
Cafetal, Guanacaste
Santa Rosa3
Santa Rosa4
Escondido
Volcán Cacao
Sierra de Cuchumatanes5
Clim. Past, 5, 725–767, 2009
Long.
Latitude
Alt.
Present biome
Modern
−70.76
−70.79
−82.24
−82.52
−75.45
−82.45
−82.52
−70.85
−80.87
−79.59
−79.66
−70.83
−79.66
−79.75
−83.48
−83.72
−83.49
−83.85
−83.87
−83.85
−83.75
−84.62
−83.84
−84.63
−84.63
−84.60
−83.61
−84.11
−84.18
−84.95
−84.21
−84.45
−84.29
−84.19
−84.18
−84.8
−84.19
−83.94
−67.75
−84.18
−84
−85.11
−84.00
−84.00
−84.73
−84.71
−83.53
−85.64
−85.66
−75.36
−85.65
−85.62
−85.62
−85.61
−85.47
−91.00
8.79
8.80
8.83
8.85
8.85
8.87
8.88
8.91
9.00
9.12
9.13
9.16
9.33
9.35
9.48
9.5
9.50
9.61
9.62
9.64
9.64
9.73
9.74
9.83
9.85
9.88
9.99
10.14
10.18
10.18
10.21
10.25
10.27
10.27
10.27
10.3
10.3
10.3
10.32
10.34
10.43
10.45
10.45
10.46
10.49
10.49
10.53
10.83
10.84
10.85
10.85
10.86
10.86
10.87
10.92
15.75
3450
3250
1150
2600
3650
1630
2300
3290
100
20
20
4080
40
50
3520
2500
3480
2670
2670
2670
3340
0
3040
270
270
35
380
2840
2600
60
740
710
740
740
770
1500
2580
630
403
380
80
110
36
40
570
610
0
0
0
0
300
280
280
280
1000
2800
CGSH
CGSH
WTRF
CTRF
CGSH
WTRF
CTRF
CGSH
TSFO
TSFO
TSFO
CGSH
TSFO
TSFO
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
CTRF
TSFO
CTRF
TSFO
TSFO
WTRF
TSFO
CTRF
CTRF
TSFO
WTRF
WTRF
WTRF
WTRF
WTRF
WTRF
CTRF
WTRF
STEP
WTRF
TSFO
TSFO
TSFO
TSFO
WTRF
WTRF
TSFO
TSFO
TRFO
TRFO
TSFO
TSFO
TSFO
TSFO
WTRF
WAMF
CGSH
CGSH
WTRF
CTRF
CGSH
WAMF
CTRF
CGSH
WTRF
WTRF
WTRF
CGSH
WTRF
WTRF
WAMF
WAMF
WAMF
WTRF
CTRF
WTRF
CTRF
TSFO
CTRF
WTRF
WTRF
WTRF
WTRF
WTRF
WTRF
WTRF
WTRF
WTRF
WTRF
WTRF
WTRF
WTRF
CTRF
WTRF
STEP
WTRF
TSFO
TSFO
WTRF
WTRF
WTRF
WTRF
WTRF
TSFO
TRFO
TRFO
WTRF
TSFO
WTRF
WTRF
WTRF
WAMF
6000
18 000
CGSH
CGSH
TSFO
CGSH
CTRF
TDFO
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R. Marchant et al.: Pollen-based biome reconstructions for Latin America
751
Table 6. Continued.
Site
Sierra de Cuchumatanes4
Sierra de Cuchumatanes3
Sierra de Cuchumatanes2
Sierra de Cuchumatanes1
Lago Quexil
Lake Peten-Itza
Lago Catemaco
Zempoala
Quila
Lake Texcoco
Chalco Lake
Lake Pátzcuaro
San Jose Chulchaca
Lake Coba
Long.
Latitude
Alt.
Present biome
Modern
−91.25
−91.5
−91.75
−92.00
−89.88
−90.00
−95.00
−99.30
−99.20
−99.12
−99.00
−101.58
−90.13
−87.55
15.75
15.75
15.75
15.75
16.92
17.25
18.66
19.20
19.30
19.44
19.50
19.58
20.86
20.86
3000
3400
3600
4200
110
200
340
3100
2800
2330
2240
2044
1
100
CTRF
CTRF
WAMF
CGSH
TSFO
TDFO
TRFO
WAMF
WAMF
WAMF
WAMF
WAMF
TDFO
WAMF
CTRF
CTRF
WAMF
CTRF
WAMF
TDFO
WAMF
WAMF
WAMF
WAMF
WAMF
WAMF
TDFO
WAMF
appears important. For other sites where the reconstructed
biome does not match the potential vegetation map a series
of different explanations, particularly local site-specific factors such as human impact, can be invoked, these will be
discussed fully. Taking our modern pollen to potential vegetation calibration, and the design of the matrices that drive
it, we reconstruct vegetation at past time intervals with “cautious confidence”.
3.2
6000 14 C yr BP biome reconstruction
The biomes reconstructed at 6000±500 14 C yr BP (Fig. 8)
show relatively small patterns of change compared to the
present. 49% of the sites retain the same biome assignment as present (Table 6). In southeastern Brazil, the majority of the sites that were previously assigned to the warm
temperate forest biome remain unchanged. A number of
sites (e.g. Serra Campos Gerais, Rio São Francisco, Aguads
Emendadas) record tropical dry forest at 6000±500 14 C yr
BP replacing tropical seasonal forest (Lagiuna Angel, Laguna Chaplin) record at the present. Sites assigned to tropical rain forest and tropical seasonal forest today mainly remain unchanged at 6000±500 14 C yr BP. Steppe continues to
be reconstructed in southeastern Argentina, as today. However, a number of sites had substantially more arboreal components at 6000±500 14 C yr BP than today, for example
Empalme Querandı́es and Lake Valencia show a transition
from steppe to tropical dry forest. An expansion of steppe
is recorded at sites previously assigned to cold mixed forest
on the Cordillera de los Andes. Unlike sites in southernmost South America that similarly record steppe, these sites
also contain significant amounts of Alnus and Podocarpus indicative of “parkland” at this time. On closer inspection of
the affinity scores, sites record an increased affinity to cool
temperate rain forest, primarily due to increased amounts
www.clim-past.net/5/725/2009/
6000
18 000
WEFO
WEFO
WAMF
WAMF
WAMF
WAMF
WAMF
WEFO
WEFO
WAMF
WAMF
WAMF
of Nothofagus pollen, although this was not sufficiently numerous to produce a cool temperate rain forest assignment.
Southernmost South America continues to have a mixture of
cool mixed forest, cool grass/shrubland, steppe and cool temperate rain forest biomes, the latter being dominant. Along
the southern Andean spine, the assignments do not differ
greatly from the modern assignment. There are broadly similar assignments to the present at Colombian sites although
there is a slight increase in the number of cool mixed forest and cool temperate rain forest biome assignments relative
to cool grass/shrubland of the present day. The sites where
this occurs (e.g. Primevera, and Páramo de Peña Negra) are
located at high altitude and may reflect either an extension
of the forest line or increased distribution of Andean forest
that predates early human impact. Sites in coastal northern
South America show a transition to tropical dry forest and
tropical seasonal forest from steppe and tropical dry forest
respectively, both indicative of a relatively mesic environment. A number of sites on the Yucatán peninsula show a
clear distribution of warm evergreen forest at 6000±500 14 C
yr BP changing from the warm mixed forest and tropical dry
forest reconstruction for the present day. These transitions
are not recorded everywhere, for example sites located in the
Mexican highlands retain the same biome assignment at the
present – warm mixed forest.
3.3
18 000 14 C yr BP biome reconstruction
Vegetation at 18 000±1000 14 C yr BP was substantially
different from the present-day, or that reconstructed at
6000±500 14 C yr BP (Fig. 9). The intensity of this vegetation transformation is demonstrated by 74% of the sites
change the biome assignment relative to the two previous
periods. In Amazonia, tropical seasonal forest and tropical dry forest is recorded instead of tropical rain forest
Clim. Past, 5, 725–767, 2009
Figure 6. Latin America plant functional types: (tropical rain green tree (a), tropical evergreen tree (b),
temperate summer-green tree (c), tropical xerophytic tree / shrub (d), arctic shrub (e), desert shrub (f),
752 dry tropical rain green tree (h), desert shrub R.
et al.:(i),
Pollen-based
reconstructions
for Latin America
(i),Marchant
tropical forb
mangrove biome
(j), xerophytic
forb (k),
tropical forb (l), temperate forb (m, n), grass (o), alpine forb (p), aquatic (q), desert forb (r, s) grass (t).
aa
b
c
e
f
g
h
i
j
k
l
m
n
o
p
q
r
s
d
t
Fig. 6. Latin America plant functional types: (tropical rain green tree (a), tropical evergreen tree (b), temperate summer-green tree (c), tropical xerophytic tree/shrub (d), arctic shrub (e), desert shrub (f), dry tropical rain green tree (h), desert shrub (i), tropical forb (i), mangrove (j),
xerophytic forb (k), tropical forb (l), temperate forb (m, n), grass (o), alpine forb (p), aquatic (q), desert forb (r, s) grass (t).
Clim. Past, 5, 725–767, 2009
www.clim-past.net/5/725/2009/
Figure 9. Biome reconstruction at 18,000 ± 1000 14C yr BP from radiocarbon dated fossil
pollen.
R. Marchant
et biomes
al.: Pollen-based
biome
reconstructions
for Latin America
Figure 7. Modern
reconstructed from surface
pollen
data (core top, trap, surface
753
sediment) used to compare against the potential vegetation (Figure 2).
Fig. 7. Modern biomes reconstructed from surface pollen data (core
top, trap, surface sediment) used to compare against the potential
vegetation (Fig. 2).
Fig. 9. Biome reconstruction at 18 000±1000 14 C yr BP from radiocarbon dated fossil pollen.
Figure 8. Biome reconstruction at 6000 ± 500 14C yr BP from radiocarbon dated fossil
pollen.
plains why the northernmost site in this cluster records cool
temperate rain forest. Sites in southeastern Brazil record a
transition from tropical dry forest to tropical seasonal forest and cool grass/shrubland. Sites in Amazonia record
mainly tropical seasonal forest, warm temperate rain forest
or steppe; this combination indicating relatively mesic forest. In the Colombian lowlands, tropical dry forest continues
to be assigned whereas Colombian highland locations reflect
a marked change from cool temperate rain forest and cool
mixed forest to the cool grass/shrubland biome. Sites in Central America show a change from tropical seasonal forest to
tropical dry forest, e.g. El Valle. The Mexican highland sites
remain unchanged, continuing to support warm mixed forest
with the pollen records being dominated by Pinus and Quercus.
4
Fig. 8. Biome reconstruction at 6000±500 14 C yr BP from radiocarbon dated fossil pollen.
or tropical seasonal forest reconstructed for the present.
A site on the present southern Amazonian boundary (Laguna Chaplin) records tropical seasonal forest. Sites in
southern South America nearly all show a transition from
cool mixed forest biomes and cool mixed forest to cool
grass/shrubland and cool grassland. However, within this
homogenous reconstruction a number of sites have a relatively high affinity to the cool temperate rain forest biome,
due to a mix of Donartia and Nothofagus pollen: this exwww.clim-past.net/5/725/2009/
4.1
Discussion and conclusions
Modern reconstruction
Previous applications of the biomisation method in Africa
(Jolly et al., 1998a; Elenga et al., 2000), China (Yu et
al., 1998, 2001), Australia (Pickett et al., 2004), Eastern
North America (Williams et al., 2000), Eurasia (Tarasov et
al., 1998a), Europe (Prentice et al., 1996a, b; Tarasov et
al., 1998a, b; Elenga et al., 2000), Japan (Takahara et al.,
2001) and Western North America (Thompson and Anderson, 2000) demonstrate that technique is able to translate
multi-site pollen data to coarse resolution vegetation reconstructions that works well over a range of vegetation types.
The Latin American results presented here provide a further
test of this ability. The ability of the biomisation method to
Clim. Past, 5, 725–767, 2009
754
R. Marchant et al.: Pollen-based biome reconstructions for Latin America
reconstruct biomes derives in part from the relatively coarse
vegetation classification (Fig. 2); which conceals significant
intra-biome variation; for example, we do not distinguish
subtypes of the warm evergreen forest biome which contains
Araucaria in southern and southeastern Brazil and Podocarpus in the northern Andes. The success of the biomisation technique is in part be due to reconstructions being carried out at a regional scale, allowing the methodology to be
adapted to the local flora, bioclimatic gradients and pollen
spectra. For example, the treatment of Quercus pollen in
Latin America is quite different from that in a European
context. Similarly, in Africa Podocarpus is assigned to the
warm temperate broad-leaved evergreen PFT (Jolly et al.,
2001), although this taxon is a coniferous needle-leafed tree,
in Latin America it is assigned to cool and intermediate temperate conifers. This regional focus also allows the pollen
to plant functional type allocations to be based on good ecological information concerned with environmental tolerances
to growth limits and an understanding of how representative
the pollen is of the surrounding vegetation. This is particularly important as the pollen taxa identified to the generic
level (the taxonomic level usually identified to) exhibit considerable plasticity in their growth form and environmental
tolerance. For example, within the genus Cordia, commonly
a woody shrub of open thorn woodland, of the northern Andes (Cleef and Hooghiemstra, 1984) two species of Cordia
are herbs in cerrado (Pereira et al., 1990; Sarmiento, 1975),
the genus is also present (C. lomatoloba and C. sagotii) in
Amazonian terra firme forest and Guyanese lowland rain forest (Steege, 1998). Furthermore the specific nature of pollen
production, dispersal and incorporation into a sedimentary
environment exhibits considerable variability that is part dependent on site characteristic. All these factors have a bearing on the results and need to be considered in the designing
of the input matrices into the biomisation process and interpretation of results.
Biomes are mainly accurately reconstructed for the
present-day even though large areas of Latin America are
covered with vegetation that has been altered by a long history of human land use (Behling, 1996; Binford et al., 1987;
Fjeldså, 1992; Gnécco and Mohammed, 1994; Gnécco and
Mora, 1997; Marchant et al., 2004; Northrop and Horn,
1996). One possible reason for this may relate to the nature of the “modern” samples. Within our analysis the modern samples are largely derived from sedimentary columns
rather than surface trap pollen data, and hence they may stem
from the last 500 years and be reflective of a period prior
to intensive human-induced change. However, the signal of
vegetation clearance does impact on the modern reconstruction as shown by the large number of sites recording cool
grass/shrubland, particularly at lower and mid-altitudes that
should support cool mixed forest or cool temperate rain forest. These assignments are thought to result from human
impact with the pollen spectra being dominated by Poaceae
and hence recording more open vegetation. To quantify the
Clim. Past, 5, 725–767, 2009
nature of this impact, it is possible to tailor the biomisation
methodology to include elements of the pollen spectra, such
as agricultural and ruderal taxa, that may indicate human
impact (Marchant et al., 2002). The ability to reconstruct
potential, rather than actual vegetation, may also relate to
the type of impact; although spatially relatively widespread,
forest clearance is often only partial with many localised
patches of forest and secondary vegetation remaining. This
results in the floristic composition of the remaining vegetation, in palynological terms at least, closely reflecting the
original vegetation composition. For example, the forest surrounding the Fúquene-II site is a successional type of forest whereas the natural vegetation would be a Andean forest type dominated by Quercus and Weimannia mixed with
Croton, Oreopanax and Phyllanthus (Van Geel and Van der
Hammen, 1973). In addition to the relatively coarse potential vegetation and biome classification, mapping the highest
biome affinity score to each site as a single dot also allows
the method to be relativity robust. Although this is suitable
for the relatively coarse reconstructions necessitated by the
continental/sub-continental scale, when investigating a small
area, more information can be preserved from the analysis. Indeed at a regional scale information on sub-dominate
biomes can be kept (Marchant et al., 2001a), new more defined biomes (Bigelow et al., 2003) or at a site-specific scale
where the affinity scores to all the biomes can be retained
(Marchant et al., 2001b, 2002b).
Despite the overall agreement between potential and reconstructed biomes a number of locations show anomalies.
Due to the floristic and structural similarities between warm
and cool grasslands (Tarasov et al., 1998a), grass-dominated
biomes can be particularly difficult to distinguish from one
another. Differentiation is possible by the other plants within
steppe and cool grass/shrubland, although there remains a
high affinity score to the cool grass/shrubland at low altitudes
with the reverse for the steppe biome at high altitudes. Another facet is that some lowland sites show reconstructions of
highland biomes, e.g. sites in central Panama and Amazonia
recording warm temperate rain forest. This result is driven
by the presence of genera that are typical of montane vegetation, e.g. Hedyosmum, Podocarpus and Quercus. A possible explanation for the presence of these highland elements
is that they are relictual; relatively isolated today they were
previously much more widespread under the glacial climate
norm of the Quaternary. This suggestion is supported by the
similarity, at a generic level, of the flora in highland Brazil
and the northeastern Andes, and the isolated patches of savanna within Amazonian forest and the Brazilian cerrado.
Furthermore, it is interesting that the presence of highland
elements appears to be greater when the moisture levels are
high. For example, within the Chocó Pacific region, where
rainfall exceeds 15 000 mm yr−1 , montane elements appear
more common than within Amazonia (Cleef, personal communication).
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R. Marchant et al.: Pollen-based biome reconstructions for Latin America
Notwithstanding some of the anomalies mentioned, the
biomisation method applied to Latin American pollen data
can reconstruct large-scale vegetation patterns despite many
pollen taxa having different ecological interpretations under
different environmental settings (Grabandt, 1980), representation of parent vegetation by pollen likely to be subject to
inter-annual variability (Behling et al., 1997b), and tropical
vegetation is difficult to reconstruct through pollen assemblages (Bush, 1991; Mancini, 1993; Bush and Rivera, 1998;
Behling et al., 1997). These factors demonstrate the importance of basing the input matrices for the biomisation process
on all the available ecological information that allowing for
the multiple assignment of the pollen taxa to the PFTs.
4.2
4.2.1
Late Quaternary biome change and palaeoenvironmental interpretations
6000±500 14 C yr BP
Compared to the present, the sites at 6000±500 14 C yr
BP record either the same biome or one indicating more
xeric vegetation. Dry environmental conditions in southern Brazil extend from the early Holocene until approximately 4500 14 C yr BP when there was an increase in arboreal taxa (Alexandre et al., 1999). Maximum aridity in
southeast Brazil was reached between approximately 6000
and 5000 14 C yr BP, prior to the transition to a modern climate (Behling, 1997a). The driest phase in central Brazil is
at approximately 5000 14 C yr BP; relatively moist climate
conditions similar to today setting in after 4000 14 C yr BP
(Ledru, 1993; Marchant and Hooghiemstra, 2004). Although
fire has been proposed as being responsible for late Holocene
variation in the forest/savanna boundary in Brazil (Vernet et
al., 1994; Desjardins et al., 1996; Horn, 1993), this relative
aridity is thought to reflect an extended dry season during
this period (Behling, 1997b). An extended dry season may
explain why Araucaria-dominated forest were still restricted
in their distribution relative to the modern day, not significantly increasing in range until approximately 3000 14 C yr
BP (Behling, 1997a). From our analysis the temporal perspective is missing, hence, we are unable to indicate if the
vegetation reflects a stable dry period, or a period where
there are alternating periods of dry and humid climates linked
for example to El Niño activity (Martin et al., 1993; Sifeddine et al., 2001). A relatively dry phase is also recorded in
northwestern Argentina between 7500 and 5800 14 C yr BP
(Schäbitz, 1991). Although pollen assemblages do not lend
themselves well to distinguishing moisture from temperature
changes, stable hydrogen isotope analysis on mosses show
the vegetation of southern South America is highly sensitive
to changes in moisture regime (Pendall et al., 2001). The predominance of steppe in southeastern Argentina agrees with
the reconstruction by Prieto (1996): steppe characterising
the area between 7000 and 5000 14 C yr BP. Locally high
moisture levels at sites closer to the Atlantic Ocean (Prieto,
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1996) may explain why sites under strongest maritime influence (Aguads Emendadas, Cerro La China) changes from
steppe to tropical dry forest as the local environment is able
to support more arboreal taxa. In soutwestern Patagonia a
sustained increase in Nothofagus pollen has been detected
from around 6800 yr BP thought to result from locally increased moisture levels (Villa-Martinez and Moreno, 2007)
Locally increased moisture levels in this part of Latin America during the mid Holocene are though to stem from intensification of the southern Westerlies (Gilli et al., 2005).
Farther west, cool temperate rain forest assignments indicate a similar climatic regime and the maintenance of Valdivian rain forest (Villigran, 1988). A dry phase is also
recorded at many Andean sites, for example, in northern
Chile desiccation of the Puna ecosystem is recorded between
8000 and 6500 14 C yr BP (Baied and Wheeler, 1993; Villigran, 1988). In lowland Chile, the period of maximum
aridity occurred between 9400 and 7600 14 C yr BP with
drier than present conditions continuing until 5000 14 C yr BP
(Heusser, 1982), this could explain the increased presence of
steppe at sites along the southern Andes. On the central Peruvian Andes, a dry warm climate was encountered between
7000 and 4000 14 C yr BP (Hansen et al., 1994). δ 18 O measurements from an ice core record taken from highland Peru
show that mid-Holocene climatic warming and drying was
recorded from 8200 to 5200 14 C yr BP with maximum aridity between 6500 to 5200 14 C yr BP (Thompson et al., 1995).
Farther north on the Bolivian Andes, a dry phase is recorded
from approximately 5500 14 C yr BP (Abbot et al., 1997). The
slight increase in the number of arboreal biome assignments
at northern Andean sites can be interpreted as an up-slope
shift of forest line. This conforms to the suggestion based on
pollen data by van Geel and van der Hammen (1973) that the
vegetation zones in the northern Andes were several hundred
of meters higher than the present at approximately 6000 14 C
yr BP. Relatively dry conditions have also been indicated for
lowland Colombia for the mid-Holocene although the onset
of dry conditions varied considerably between sites – occurring between 6500 and 4500 14 C yr BP (Behling et al., 1999).
Added complexity is caused by steep environmental gradients associated with non-climatic factors. For example, the
presence of the tropical dry forest biome in lowland Colombia, e.g. the catchment of El Piñal, results from a combination of strongly seasonal conditions at present and locally
strong edaphic influence (Behling and Hooghiemstra, 1999).
Farther north, the assignment of Lake Valencia to the tropical dry forest is in agreement with the site-specific interpretation that more arboreal taxa (Bursera, Piper and Trema)
were present after approximately 10 000 14 C yr BP due to
the onset of a more humid climate (Bradbury et al., 1981):
these tropical raingreen taxa are indicative of a seasonal climate with relatively dry conditions. This appears to be a regional signal as early Holocene evergreen forests of northern
Venezuela were replaced by semi-deciduous elements during
the mid-Holocene (Leyden, 1984). Enhanced precipitation
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R. Marchant et al.: Pollen-based biome reconstructions for Latin America
over Central America would have been accompanied by a
northward shift of the ITCZ, enhanced southerlies and cooler
equatorial sea surface temperatures (Harrison et al., 2003).
Low lake levels in central Panama also indicate that environmental conditions at this period were more xeric (Piperno et
al., 1991b; Bush et al., 1992) whereas sites on the Yucatán
peninsula show a shift to warm evergreen forest where the
warmer conditions that characterise the early Holocene persisted until approximately 6500 14 C yr BP (Brown, 1985).
This result may stem from locally high moisture levels as a
result of maritime influence, a similar mechanism having being proposed to explain a comparable shift in coastal Brazil
and Argentina. Despite the majority of the evidence for a
mid-Holocene dry period, there still remains a debate about
the intensity, and even the occurrence, of this. SalgadoLabouriau et al. (1998) suggests that most savanna areas
were characterised by increased rainfall between 7000 and
6000 14 C yr BP although there is considerable variation in
the timing of the onset of more humid conditions so it may
be that such a mesic period falls outside our temporal window.
One of the main mechanisms used to explain moisture
shifts is fluctuations in the Southern Oscillation and the migration of the ITCZ (Martin et al., 1997). Martin et al. (1997)
suggests that during the mid-Holocene, the ITCZ was located farther north than its present-day position (Fig. 1) –
this would produce a summer rainfall deficit and increased
winter precipitation; in short greater seasonality. Rather than
changes in the median position of the ITCZ, changes in the
character of the ITCZ oscillation, such as greater latitudinal
range for annual migration, can be invoked to explain vegetation changes (Behling and Hooghiemstra, 2001). However, due to the topographical influence of the Andes and the
convergence of westerly and easterly winds, the ITCZ has
a sinusoidal profile over northern South America (Fig. 1).
Therefore, moisture changes over northeastern South America are likely to result from the importance of convective
moisture sources; reduced precipitation, particularly in mid
latitude western South America, following reduced intensity
of westerly climate systems. It is also possible that episodic
dry events that presently occur in South America in relation
to sea-surface temperature anomalies of the Pacific Ocean
(ENSO) were more frequent in the mid-Holocene (Markgraf,
1998). This later suggestion may also have led to the increased fire frequency indicated in southeast Brazil (Alexandre et al., 1999).
The spatial relationship between Latin American and
Africa (Servant et al., 1993; Jolly et al., 1998a) warrants further investigation (Marchant and Hooghiemstra, 2004). A
particular target for the investigation could be the impact and
feedbacks of vegetation changes on climate. For example,
large changes in African vegetation about the Sahel are suggested to have been important in influencing Indian monsoon
dynamic (Doherty et al., 2000). Such a phenomena of vegetation feedbacks on the climate system appears weaker in
Clim. Past, 5, 725–767, 2009
South America than in Africa although it is likely to have
had an impact as yet unqualified. Certainly Latin America
would benefit from targeted model applications in the same
way that has been applied to Africa (Kubatzki and Claussen,
1998; Doherty et al., 2000). This modelling of climate dynamics Latin American represents a special challenge for climate models and modellers (Valdes, 2000) primarily due to
the steep environmental gradients and rapid transition from
one biome to another (Fig. 2).
4.2.2
18 000±1000 14 C yr BP
The dating of the LGM in Latin America can be problematic (Bush et al., 1990; Hooghiemstra et al., 1992; Ledru et
al., 1996, 1998; Sifeddine et al., 2001); late Pleistocene sediments often containing sedimentary gaps at, or about, the
LGM (Ledru et al., 1998), that are compounded by slow sedimentation rates. These sedimentary constraints make characterisation of the LGM vegetation highly contentious and
have fuelled debates on LGM climates spanning two decades
(Hooghiemstra and van der Hammen, 1998; Colinvaux et al.,
2000; Thomas, 2000). Indeed, it has been suggested that
some of the sites used in our analysis do not contain a sedimentary record of the LGM (Ledru et al., 1998) although due
to application of a 2000 year-wide time window, we are able
to include some of these sites with contentious sediments.
The LGM in Latin America, like most of the tropics,
was characterised by a cold dry climate (González et al.,
2008) with temperature reduced by about 4◦ C and precipitation by about 30% (Farrera et al., 1999). Ice caps were
present on the southern tip of South America which spread
onto the plains and the coastal area (Heine, 1995). Evidence from glacial moraines also indicates considerable expansion of Andean glaciers (Hollis and Schilling, 1981; Villagran, 1988; Birkland et al., 1989; Seltzer, 1990; Thouret
et al., 1997). Most of southern South America was characterised by an erosional environment; locations that would
later accumulate sediments were glaciated, or subject to fluvial activity (Heine, 2000). This situation is recorded by
ice cores from the high Andes that contain large amounts
of dust about the LGM, this being derived from surrounding deflating desert areas (Thompson et al., 1995). This
cold, arid environment is clearly reproduced by the vegetation which shows a transformation from the cool temperate rain forest to the cool grass/shrubland biome. Although
Nothofagus-dominated forest is thought to have been extirpolated from coastal Chile at the LGM (Hollis and Schilling,
1981), fossil beetle assemblages in basal peat from Puerto
Eden (49◦ S, 74◦ W) indicate that Nothofagus-dominated forest survived glaciation within the Chilean channels (Ashworth et al., 1991). An earlier date of deglaciation of the
Taitao Peninsula indicates there was relatively local migration from Chiloé Island that may explain the rapid re-growth
of Nothofagus-dominated forest (Lumley and Switsur, 1993).
Along the Chilean Pacific coast the present cool evergreen
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R. Marchant et al.: Pollen-based biome reconstructions for Latin America
forest was shifted approximately 5◦ northwards relative to
the present day; not as a discrete forest type but as a parkland type vegetation mosaic (Villagran, 1988), not forming closed forests until the early Holocene (Schäbitz, 1994;
Heusser, 1995). This vegetation is evidenced with the analysis presented here by the northernmost site (Laguna Six Minutes) recording cool temperate rain forest. However, it is
unlikely this represents closed forest persisting in the area,
trees being present within a woodland/steppe vegetation mosaic (Villagran, 1988). The rate of spreading of this forest
into the Holocene would probably have been strongly dependent on the density of the parent plants from the initial seeding fraction (Huntingford et al., 2000). The maintenance of
cool temperate rain forest taxa, albeit at relatively low levels, may result from high moisture levels as recorded by high
lake stands at this time (Markgraf et al., 2000). These may
reflect outbreaks of polar air and subsequent generation of
low-pressure systems in the western Atlantic; combined with
lower temperatures this situation would lead to a positive water balance. Indeed, the presence of relatively local moisture
sources would have been important at the LGM and allow
us to explain regional patterns of biome change outside the
influence of the ITCZ migrations (Markgraf et al., 2000).
Considering the sites along the northern Andes, it is clear
from the vegetation that climate was colder during the LGM,
reductions up to 12◦ C may have been reached at very high
altitudes (Thompson et al., 1995). A substantial temperature depression during the last glacial period is mirrored by
a significant impact on the vegetation composition and distribution. From our analysis it is apparent that the tree line
was significantly lower at the LGM, concordant with a suggested lowering of vegetation zones by approximately 1000
to 1500 m relative to the present-day position (Monslave,
1985; Wille et al., 2001). At lower elevations in western
Colombia, a more conservative depression of the vegetation
has been suggested from Timbio (Wille et al., 2000). Indeed, the spatial character of the cooling and drying in the
Neotropics is still under debate (Markgraf, 1993; Colinvaux,
1996; Hooghiemstra and Van der Hammen, 1998; Farrera et
al., 1999; Boom et al., 2002). Greater temperature change
at high altitudes compared with those at low altitudes and at
the sea surface (CLIMAP, 1976) can be explained in terms
of changes in lapse rate (Bush et al., 1990; Peyron et al.,
2000; Wille et al., 2001) or compression of vegetation belts
(Van der Hammen and Absy, 1994). The lapse-rate gradient
is partly influenced by atmospheric moisture levels (Barry
and Chorley, 1990). As precipitation was reduced at the
LGM, an overall steeper lapse rate, particularly at higher altitudes where moisture reductions would have been highest,
seems likely (Wille et al., 2001). The extent to which lapserate changes can be used to explain spatially different signals from the data must be used with caution, particularly
as most palaeoclimatic reconstructions have been carried out
with some kind of modern analogue approach (Farrera et al.,
1999). These reconstructions commonly do not take into acwww.clim-past.net/5/725/2009/
757
count non-climatic parameters which would impact on vegetation composition and distribution such as volcanic activity
(Kuhry, 1988), fire (Cavelier et al., 1998; Rull, 1999), UVB insolation (Flenley, 1998) or atmospheric composition, in
particular changing CO2 levels (Woodward and Bond, 2004).
For example, concentrations of CO2 reduced to glacial levels
(200 ppmV) have been shown to have a very significant impact on tropical vegetation (Jolly and Haxeltine, 1997; Boom
et al., 2002; Marchant et al., 2002b; Wu et al., 2007a, b).
In south-east Brazil vegetation at the LGM was characterised by tropical dry forest and tropical seasonal forest;
this latter vegetation type may have been restricted within
deep valleys and along waterways; site-specific records
from southeast Brazil indicate open grasslands (campo
limpo) with forest elements being retained as gallery forest (Behling, 1997a). Some model reconstructions of global
vegetation patterns have indicated that there was an increase
in warm evergreen forest in Brazil at the LGM at the expense of tropical seasonal forest (Prentice et al., 1993). This
pattern of change is supported by the data presented here
where plants generally found at high altitudes today were
more common in Amazonia at the LGM. Clapperton (1993)
used geomorphic data to infer a very sparsely vegetated landscape on the Brazilian Highlands, possibly relating to our reconstruction of steppe for a site in eastern Brazil. However,
the cool grass/shrubland biome appears to be a common type
of vegetation at this time. Cold climates in eastern South
America could have resulted from the incursion of polar cold
fronts that would occasionally reach northwards of the equator (Ledru, 1993; Behling and Hooghiemstra, 2001). This
phenomenon could combine with equatorward shift of polar high-pressure areas and mid-latitude cyclones resulting
in displacement and compression of the subtropical anticyclone between mid latitudes westerlies (Dawson, 1992). This
climatic regime would result in more restricted migration of
the ITCZ and pronounced aridity that would have been compounded by lower sea surface temperatures and associated
reduction in atmospheric moisture.
At altitudes of approximately 3000 m in northern Peru
vegetation about the LGM comprised a mixture of wet and
moist montane forest elements with open woodland (Hansen
and Rodbell, 1995); this vegetation association having no
modern analogue. Although the Andes remained relatively
moist at the LGM, particularly in the northern part where
the concave shape of the mountain chain entrap moisture
from the rising air (Fjeldså et al., 1997), it is not certain
what occurred in the lowland areas to the east of the Andes
(Colinvaux, 1989; Colinvaux et al., 1997; Bush et al., 1990;
Thouret et al., 1997). In the Colombian lowlands two sites
are characterised by the tropical dry forest biome, this agrees
with the suggestion from a pollen study at Rondonia that very
open savanna characterised the catchment at the LGM (van
der Hammen and Absy, 1994). Similarly, sparse vegetation
cover would have been present on the Plateau of Mato Grosso
(Servant et al., 1993) and is likely to have extended along the
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R. Marchant et al.: Pollen-based biome reconstructions for Latin America
coastal areas of Guyana and Surinam (Wijmstra, 1971) – this
scenario is supported by our analysis, i.e. a site in lowland
Panama recording tropical seasonal forest. Although the majority of the area presently covered by drier types of tropical forest would probably have been replaced by more open
woodland at the LGM, environmental changes in savannas at
the LGM appear to have been spatially complex. Whether
the drier, cooler, conditions resulted in restricted range forest
refugia cannot be answered from the available evidence although the vegetation appears heterogeneous as a mosaic of
Andean, savanna and tropical rain forest taxa combined. Indeed, this reiterates the suggestion by Colinvaux et al. (2001)
and now widely accepted within the palaeoecological community, that plants responded to Quaternary climate changes
as individuals not as biomes. Therefore, to fully investigate
vegetation response to climate change is necessary to retain
information contained within the affinity scores to the subdominant biomes (Marchant et al., 2002), or to carry out the
analysis at the PFT level. Indeed this approach would allow investigations into which elements of the vegetation were
particularly sensitive to environmental change. Expansion of
savanna could have been aided by reduced CO2 concentrations and the resultant competitive advantage attained by C4
grasses over C3 plants (Haberle and Maslin, 1999; Marchant
et al., 2002).
Within highland México, warm mixed forest continues to
be reconstructed due to the presence of Pinus and Quercusdominated forests. Although the same biome is recorded
at all time periods, it unlikely to be analogous to present
day mixed forest; this was characterised by sparsely forested
temperate scrub (Binford et al., 1987). This continued reconstruction also could be demonstrating that certain locations are unlikely to register a change due to their position
in bioclimatic space: those sites located far away from the
boundaries of adjacent biomes would need a massive climatic change to result in an ecosystem shift. Such a situation
has clearly been shown for the Holocene where the sites to
undergo biomisation show little change relative to the modern (Ortega-Rosas et al., 2008a, b). Indeed, a site located in
northwestern México records a transition from cool conifer
forest to warm mixed forest about 6000 yr BP (Ortega-Rosas
et al., 2008b). Similarly, a strong aridity signal is directly
recorded by low lake levels in central México due to reduced
northern excursion of the ITCZ, trade wind circulation, and
ensuing reduced oceanic-land moisture transfer (Markgraf et
al., 2000) that would have been reflected in ecosystem response. For example, forest on the Pacific side of the Central
America contained a mosaic of high and low altitude forest species; a similarly novel type of forest has also been
shown for Mera, Ecuador (Liu and Colinvaux, 1985) and
Peten, Guatemala (Leyden, 1984). Of the two sites that
record the warm evergreen forest biome at this period a site
in Guatemala was dominated by Chenopodiaceae, Juniperus,
Pinus and Quercus.
Clim. Past, 5, 725–767, 2009
We have presented vegetation reconstructions throughout
Latin America at 6000±500 14 C yr BP and 18 000±1000
14 C yr BP using an objective method based on biomes, constituent PFTs that are described by a set of unique pollen
spectra. As a unified methodology has been applied to the
pollen data, this reconstruction of biomes provides an objective basis for interpreting large-scale vegetation dynamics,
and the environmental controls on these over the late Quaternary and can be used as a dataset for model-data comparisons at 6000 and 18 000 yr BP. Changes at 6000±500
14 C yr BP, although relatively small, indicate a transition to
more xeric vegetation. The changes at 18 000±1000 14 C yr
BP are more homogenous and indicative of a cooler, drier
climate. These reconstructions are consistent with numerous site-specific interpretations of the pollen data. The success of the reconstruction has in part been determined by the
coarse resolution of biome definitions, and using the most
dominant biome for description and interpretation of the results. To develop understanding of vegetation response to
environmental change, and possible feedbacks, information
that is presently redundant should be retained and the results combined with climate/vegetation modelling initiatives.
It is apparent from the relatively sparse coverage, in comparison to Europe and North America, that the late Quaternary vegetation history of the Neotropical phytogeographical
realm remains still relatively poorly resolved despite its importance in model testing, developing biogeographical theory (Tuomisto and Ruokolainen, 1997), and understanding
issues concerned with biodiversity and human-environment
interactions. It has been shown that environmental change
is rarely spatially uniform and as such necessitates an even
greater number of sites to determine more precisely this complexity and the driving mechanisms behind this. New sites,
located in key areas, combined with the application of a
range of proxies of environmental change, are required to refine our understanding of Neotropical ecosystem responses
to late Quaternary climatic variations.
Acknowledgements. This research has been funded by the
Netherlands Organisation for Scientific Research (NWO) under
award 750:198:08 to Henry Hooghiemstra/Rob Marchant. Rob
Marchant recent contribution being supported by EU Grant No:
EU-MXC-KITE-517098. This paper contributes to the BIOME
6000-research initiative which is a community-wide collaboration
that started in 1994 under four elements of the International
Geosphere-Biosphere Program (IGBP-GAIM, IGBP-DIS, IGBPGCTE and IGBP-PAGES) with the aim to create fully documented
pollen and plant macrofossil data sets for 6000 and 18 000 14 C yr
BP. Dominique Jolly was instrumental in developing and applying
these techniques in Africa and subsequent application to different
geographical regions – indeed the Latin American application
presented here would not have been possible without this massive
contribution and encouragement throughout the development
of this work. In particular we thank Sandra Diaz, Liz Pickett,
Colin Prentice and Bob Thompson for comments on earlier drafts
of this manuscript. C. Peñalba and an anonymous reviewer are
thanked for insightful reviews that have improved the manuscript
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R. Marchant et al.: Pollen-based biome reconstructions for Latin America
and some of the associated figures. Ary Teixeira de Oliveira-Filho
and Olando Rangel are particularly thanked for comments on the
ecology of the Latin American taxa. Kirsten Sickel, Gerhard Boenish and Silvana Schott are thanked for help in producing the figures
and archiving the results. Particular thanks must go to Eric Grimm,
John Keltner and Vera Markgraf for their energies in establishing,
and developing, the Latin American Pollen Database. At the very
centre of this work are all those palynologists who have supplied
data to the LAPD, or specifically to be used for this paper; without
their efforts such syntheses would not be possible.
Edited by: R. Bonnefille
Publication of this paper was granted by
EDD (Environnement, Développement Durable)
and INSU (Institut des Sciences de l’Univers) at CNRS.
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